aniversário do Prof
XIV Escola de Modelos de Regressão
Campinas
2015
ABE
De 2 a 5 de Março de 2015
Centro de Convenções
Unicamp, Campinas, SP , Brasil
Mensagem de Boas Vindas
É com imensa satisfação que o Departamento de Estatística da Universidade Estadual de Campinas promove à XIV Escola de Modelos de Regressão, um evento de elevado nível científico que contará com participantes nacionais e internacionais.
O programa da XIV EMR inclui 12 Conferências (6 nacionais e 6 internacionais), dois minicursos (MC1 e MC2), 24 Comunicações Orais (CO) e 2 Sessões de Pôsteres, 3 Sessões Temáticas, 4 Sessões de Jovem Doutor, 2 tutoriais e 1 workshop.
A Comissão Organizadora dá as Boas Vindas à todos os participantes que irão prestigiar o evento e espera que o mesmo constitua uma oportunidade para a divulgação de trabalhos relevantes desenvolvidos por pesquisadores nacionais e estrangeiros do mais alto nível, sendo assim uma oportunidade de interação entre alunos, profissionais e pesquisadores da área de Modelos de Regressão e áreas afins.
APRESENTAÇÃO
Mestre pelo Departamento de Estatística da Universidade de São Paulo e doutor em Estatística pelo Departamento de Estatística da Universidade da Califórnia, Berkeley. Atualmente é professor titular aposentado junto ao Departamento de Estatística da Universidade de São Paulo e Pesquisador 1Ado CNPq .Até o presente momento, publicou mais de 170 artigos (CV-LATTES) em periódicos internacionais indexados, a maioria tendo como coautores seus alunos de mestrado e doutorado. Orientou, até o presente momento, 15 alunos de mestrado e 38 de doutorado. Juntamente com o professor Wilton Bussab, foi o vencedor do prêmio Jabuti 2006 em Ciências por "Elementos deAmostragem". Recebeu o prêmioABE- 2012, outorgado pelaAssociação Brasileira de Estatística.
Prof. Heleno Bolfarine
Departamento de Estatística
Instituto de Matemática, Estatística e Computação Científica da UNICAMP
A Comissão Organizadora da XIV Escola de Modelos de Regressão (XIV EMR) agradece ao apoio das seguintes instituições: CAPES, FAPESP, CNPq, Associação Brasileira de Estatística (ABE) , Fundo de Apoio ao Ensino, à Pesquisa e à Extensão (FAEPEX) e o Instituto de Matemática, Estatística e Computação Científica (IMECC) da UNICAMP, bem como á todas todas as Fundações Estaduais, Instituições e Programas de Pós-Graduação do Brasil, que possibilitaram a participação de pesquisadores, estudantes e profissionais no evento.
AGRADECIMENTOS
COMISSÕES
Comissão Organizadora local (Unicamp):
Aluísio de Souza Pinheiro Víctor Hugo Lachos Dávila Caio Lucidius Naberezny Azevedo Filidor Edilfonso Vilca Labra Hildete Prisco Pinheiro Comissão Científica:
Víctor Hugo Lachos Dávila – Unicamp Mario de Castro Andrade Filho ICMC – USP Renato Martins Assunção – UFMG
Dipankar Bandyopadhyay – University of Minnesota, USA Jorge Luis Bazán Guzmán ICMC – USP
Celso Rômulo Barbosa Cabral – UFAM Vicente Garibay Cancho ICMC – USP
Luis Mauricio Castro Cepero – Universidad de Concepción, Chile Francisco Cribari Neto – UFPE
Somnath Datta – University of Louisville, USA Edwin Moises Marcos Ortega ESALQ – USP Suporte Técnico
Luis Enrique Benites Sánchez IME – USP Rocío Paola Maehara Sánchez IME – USP
http://www.ime.usp.br/~abe/emr2015/paginas/comissoes
Entrega de Material e Inscrições 13:00 14:30
15:00 16:30
16:30 17:30 14:30 15:00
Programação 02/03/2015
T1 :StatSoft:
Josias Oliveira, Statsoft R and Google Maps:
T2 :
Marcos Oliveira Prates, UFMG
Código QR da página web com os resumos dos Tutoriais:
W1:Modelos de Regressão em Julia Luis Benites Sánchez, IME-USP
Código QR da página web com mais informações do Workshop
Legenda Legenda
Inicio Final
Workshop Tutorial
Café Estatístico T1
Auditório 2
T2
Auditório 2
W1
Auditório 2
Programação 03/03/2015
Café Estatístico
Almoço
Café Estatístico 08:10 08:40
08:40 10:10 10:10 10:30 10:30 11:30 11:30 12:30 12:30 14:00 14:00 15:00 15:00 16:00 16:20 16:20 17:30
17:30 19:00 16:00 Inicio Final
ST1.1: Susmita Datta ST1.2: Glen Satten ST1.3: Somnath Datta
ST1: Big Data MC: Minicurso
MC1: Modelos de Regressão Log-simétricos em R 2: Misturas Finitas de Distribuições Assimétricas MC
Conferências 1: Reinaldo Arellano-Valle CF
CF2: Josemar Rodrigues CF3: Dipankar Bandyopadhyay CF4: Francisco Cribari-Neto
Comunicação Oral CO1: Erro nas variáveis CO2: Modelos inflacionados CO3: Séries temporais
Sessão Pôster Local: Saguão IMECC - Unicamp MC1
Auditório 1
MC2
Auditório 2 ST1
Auditório 3
Cerimônia de Abertura
Auditório 3
Conferência de Abertura (Prof. Heleno Bolfarine) -Auditório 3
CF1
Auditório 3
CF2
Auditório 1
CF3
Auditório 3
CF4
Auditório 1
CO1
Auditório 1
CO2
Auditório 2
CO3
Auditório 3
PO1
Saguão do IMECC (com Coquetel)
Programação 04/03/2015
Café Estatístico
Almoço
Café Estatístico 08:10 08:40
08:40 10:10 10:10 10:30 10:30 11:30 11:30 12:30 12:30 14:00 14:00 15:00 15:00 16:00 16:20 16:20 17:30
17:30 19:00 16:00 Inicio Final
Prof. Renato Assunção ST2.1: Thaís Paiva ST2.2: Marco Ferreira ST2.3: Andrew Lawson ST2: Estatística Espacial MC: Minicurso
MC1: Modelos de Regressão Log-simétricos em R 2: Misturas Finitas de Distribuições Assimétricas MC
Conferências 9: Tsung-I Lin CF
CF10: Artur José Lemonte CF11: Peter Mueller CF12: Hildete Pinheiro
Comunicação Oral CO4: Birnbaum Saunders
Conferências 5:
CF Somnath Datta CF6: Silvia Ferrari CF7: Dipak Kumar Dey CF8: Francisco Louzada Neto
Sessão Pôster Local: Saguão IMECC - Unicamp MC1
Auditório 1
MC2
Auditório 2 ST2
Auditório 3
CF5
Auditório 1
CF6
Auditório 2
CF7
Auditório 1
CF8
Auditório 2
CF9
Auditório 1
CF10
Auditório 2
CF11
Auditório 1
CF12
Auditório 2
JD1- Auditório 1
JD2- Auditório 1
JD3- Auditório 2
JD4- Auditório 2
CO4
Auditório 3
PO2
Saguão do IMECC
Jovem Doutor
JD1: Denise Reis Costa (INEP – MEC) 2: Rafael Izbicki (DE – UFScar) JD
3: Aldo Medina Garay (IMECC – Unicamp) JD
4: Erica Castilho Rodrígues (ICEB-UFOP) JD
Programação 05/03/2015
Inicio Final
Comunicação Oral CO5: Métodos bayesianos CO6: Modelos de efeitos mistos CO7: Distribuição logística
extensões e aplicações CO8: Modelos de sobrevivência Prof. : P. Morettin
ST3.1: Michel H. Montoril ST3.2: Rogério F. Porto ST3.3: Ronaldo Dias ST3: Métodos não paramétricos
Café Estatístico 08:10 08:40
08:40 10:10
10:20 10:40 10:40 11:20 11:20 12:30 10:10 10:30
CO6
Auditório 2
CO5
Auditório 1
CO7
Auditório 1
CO8
Auditório 2
ST3
Auditório 3
Cerimônia de Encerramento-Auditório 3
Encerramento
(Prof. Gauss Cordeiro)- Auditório 3
Minicurso 1 ( MC 1)
Resumo: In the context of regression models, the data for which the response variable is continuous, strictly positive, and asymmetric with possible outlying observations are commonly employed in various fields of practice. Being so, this minicourse provides a unified theoretical framework of semi-parametric regression analysis based on log-normal, log-Student-t, Birnbaum-Saunders, Birnbaum- Saunders-t, harmonic law and other right-skewed, heavy/light-tailed and strictly positive distributions, in which both, the median and the skewness of the response variable distribution are explicitly modeled. In this setup, here termed log- symmetric regression models, both the median and the skewness are described using semi-parametric functions of explanatory variables, in which their nonparametric components are approximated by natural cubic splines or P-splines. An iterative process of parameter estimation based on Fisher scoring, expectation-maximization and backfitting algorithms is described.
The behavior of the (penalized) maximum likelihood estimates is illustrated by using simulation experiments. A computational implementation of the proposed methodology in the R statistical computing environment is also presented. The attractive features of this package include the possibility of performing residual analysis by applying deviance-type residuals for median and skewness submodels, as well as sensitivity studies through local influence under usual perturbation schemes. Five real data sets are analized to illustrate the flexibility of the addressed statistical and computational tools.
Público alvo: estudantes de mestrado, doutorado.
Modelos de Regressão Log-simétricos em R Luis Hernando Vanegas (IME USP/ ) Gilberto Alvarenga Paula (IME USP/ )
8:10 ate 10:10 Data e Horário
Março
3e4
Auditório 1
Minicurso 2 ( MC 2)
Resumo: Misturas finitas de distribuicões são utilizadas em situações onde existe heterogeneidade não observável na população. Por exemplo, suponhamos que imagens de células cancerígenas sejam objeto de estudo. Neste caso a variável tipo do tumor, classificada em malígno ou benigno, não é observável diretamente. Para classificar a célula em uma das duas categorias, usualmente observamse variáveis como raio, a textura e o perímetro do núcleo celular, dentre outras (Street et al., 1993). Misturas finitas também constituem uma família extremamente flexível de distribuições, útil para modelar dados que apresentam comportamento não usual, apresentando ao mesmo tempo assimetria, caudas pesadas e observações aberrantes. Os modelos de misturas finitas tem sido objeto de investigação intensa nos últimos anos. Existem aplicações em diversas áreas, como biologia, engenharia, marketing e medicina, somente para citar algumas. Alem disso, uma vasta bibliografia está disponível, como os textos de Bohning (2000), McLachlan & Peel (2000), Fruhwirth-Schnatter (2006), Schlattmann (2010) e Mengersen et al. (2011), alem das edições especiais do periódico Computational Statistics and Data Analysis (Bohning et al., 2007, 2014).
Neste minicurso pretendemos apresentar os principais aspectos inferenciais em misturas finitas de distribuições, tanto no contexto Bayesiano quanto no contexto frequentista, alem de discutir alguns temas recentes de pesquisa na área, com destaque para aqueles que vem sendo desenvolvidos pelos autores desta proposta.
Público alvo: estudantes de graduação e pós graduação
Mistura Finita de Distribuições Assimétricas Camila Borelli Zeller (UFJF) Celso Rômulo Barbosa Cabral (UFAM)
Víctor Hugo Lachos (UNICAMP)
8:10 ate 10:10 Data e Horário
Março
3e4
Auditório 2
Conferência de abertura
Resumo: In this talk we investigate maximum likelihood estimation in linear functional relationships with replications. The general formulation considered in Dorff and Gurland (1961) is studied. The approach is based on Mak (1982) where general results for maxi- mum likelihood estimation in the presence of incidental parameters are considered. Since the approach allows the derivation of the asymptotic covariance matrix of the maximum likelihood estimators of the model parameters it is possible to compute the asymptotic relative efficiencies of the maximum likelihood estimators with respect to the estimators suggested in Dorff and Gurland (JRSS B-1961). Computation of maximum likelihood estimators is discussed. Comparisons are also reported for the situation of a small sample selected from a particular generated population. The methodology is illustrated with a real data set.
Key words: Linear functional relationships; Replicated observations; Maximum likelihood estimation;Asymptotic normality.
Consistent estimation in functional relationships with replicates Heleno Bolfarine
IME - USP
11 3: 0 ate 1 : 02 3 Data e Horário
Março
3
Auditório 3
Conferência 1 (CF1)
Resumo: The entropy and other measures related to mutual information and/or divergence between random vectors, such as the Shannon index and the Kullback- Leibler divergence, have been widely studied in the case of the multivariate normal distribution. We extend these tools to the more flexible families of multivariate skew-elliptical distributions. We study in detail the cases of the multivariate skew- normal and skewt distributions. We illustrate our findings in the context of two real applications.
Entropy-based measures for multivariate skew-elliptical distributions Reinaldo Arellano-Valle
Pontificia Universidad Católica de Chile
14 0: 0 ate 1 : 05 0 Data e Horário
Março
3
Auditório 3
Conferência 2 (CF2)
Resumo:This paper deals with the Bayesian machinery for the estimation of the parameters of the correlated Binomial distribution which was generated from a nite correlated Binomial processes to solve dispersion problems. This model is a restricted version of the Conway-Maxwell-Binomial (CMB) distribution introduced by Shmueli et al. (2005) which is the correlated Binomial distribution (CB) discussed in Kupper & Haseman (1978) and Bahadur (1961) if and only if some restrictions are imposed on the parameters. These restrictions give to CMB distribution the Bayesian ability to control the phenomenon of dispersion in count data toward the binomial scenery by means of the number of the correlated Bernoulli variables. An illustrative example with real data shows the usefulness of the proposed restricted model.
The Bayesian ability of the restricted Conway-Maxwell-Binomial model to control dispersion in discrete data
Josemar Rodrigues ICMC – USP
14 0: 0 ate 1 : 05 0 Data e Horário
Março
3
Auditório 1
Conferência 3 (CF3)
Resumo: Dental caries data consist of two levels of hierarchy, a tooth level and a surface level, and outcomes often exhibit spatial structures among neighboring teeth and surfaces (i.e. the disease/decay status of a tooth or surface might be influenced by the decay status of a group of neighboring teeth/surfaces). Assessments of dental caries at the tooth-level yield binary outcomes (presence/absence of teeth) and assessments at the surface-level yield trinary outcomes, indicating the healthy, decayed, or filled surfaces. The presence of these mixed discrete outcomes complicates data analysis within a unified framework. To mitigate these, we develop a Bayesian two-stage model under suitable Markov random field assumptions that accommodates the natural hierarchy within the mixed responses. In the first stage, we focus on estimating the degree of spatial association between existing and missing teeth using an autologistic model. In the second stage, we quantify spatial associations among surfaces on the existing teeth using a Potts model. Both models include random effects term to adjust for the data hierarchy involved.
Computational difficulty due to the intractable normalizing constant is tackled using an approximate exchange sampler. We illustrate the potential of our methodology using simulation studies and application to a dataset obtained from a clinical study on dental caries.
This is joint work with Ick Hoon Jin (Ohio State University) and Ying Yuan (M D Anderson Cancer Center).
A Bayesian two-stage model for multivariate discrete spatial data with applications to dental caries
Dipankar Bandyopadhyay University of Minnesota, Minneapolis, USA
15 0: 0 ate 1 : 06 0 Data e Horário
Março
3
Auditório 3
Conferência 4 (CF4)
Resumo:Nessa conferência serão apresentados os resutados de uma análise de regressão baseada em dados internacionais sobre determinantes da aceitação da homossexualidade. Em particular, será medido o impacto que a inteligência exerce sobre a aceitação da homossexualidade. Curvas de impacto são obtidas e inferência bootstrap é realizada.
Aceitação da homosexualidade e inteligência: evidência internacional Francisco Cribari Neto
DE – UFPE
15 0: 0 ate 1 : 06 0 Data e Horário
Março
3
Auditório 1
Conferência 5 (CF5)
Resumo: We discuss how to extend parametric and nonparametric inference procedures when the classical assumption of independence is violated due to clustering. Clustered data arise in a number of practical applications where observations belonging to different clusters are independent but observations within the same cluster are dependent.
While making adjustments for possible cluster dependence, one should also be aware of the informative cluster size phenomenon which occurs when the size of the cluster is a random variable that is correlated to the outcome distribution within a cluster, often through a cluster specific latent factor. We demonstrate the correct inference procedures under various scenarios.
Marginal Regression Models for Clustered Data Inference When the Cluster Size is Potentially Informative
Somnath Datta University of Louisville, USA
10 3: 0 ate 1 : 01 3 Data e Horário
Março
4
Auditório 1
Conferência 6 (CF6)
Resumo:We introduce and study the Box-Cox symmetric class of distributions, which is useful for modeling positively skewed, possibly heavy-tailed, data. The new class of distributions includes the Box-Cox t, Box-Cox Cole-Green, Box-Cox power exponential distributions, and the class of the log-symmetric distributions as special cases. It provides easy parameter interpretation, which makes it convenient for regression modeling purposes. Additionally, it provides enough flexibility to handle outliers. The usefulness of the Box-Cox symmetric models is illustrated in a series of applications to nutritional data.
Joint work with Giovana Fumes.
Box-Cox symmetric models and applications to nutritional data Silvia L. P. Ferrari
Departmento de Estatística – Universidade de São Paulo, Brasil
10 3: 0 ate 1 : 01 3 Data e Horário
Março
4
Auditório 2
Conferência 7 (CF7)
Resumo: Many modern statistical problems can be cast in the framework of multivariate regression, where the main task is the estimation of a possibly high- dimensional coefficient matrix. The low-rank structure in the coefficient matrix is of intrinsic multivariate nature, which, when further combined with sparsity, can further lift dimension reduction, conduct variable selection, and facilitate model interpretation. Using a Bayesian approach, we develop a unified sparse and low- rank multivariate regression method, to both estimate the coefficient matrix and obtain its credible region for making inference. The newly developed sparsity- inducing prior for the coefficient matrix enables simultaneous rank reduction, predictor selection, as well as response selection. We utilize the marginal likelihood to determine the regularization hyperparameter, so it maximizes its posterior probability given the data. Theoretically, the posterior consistency is established under a high-dimensional asymptotic regime. The efficacy of the proposed approach is demonstrated via simulation studies and a real application on
yeast cell cycle data.
Keywords: Bayesian; Low rank; Penalized least squares; Posterior consistency;
Sparsity; Rank
Joint with Gyuhyeong Goh, and Kun Chen.
Bayesian sparse reduced rank multivariate regression Dipak Dey
University of Connecticut, Storrs, USA
11:30 ate 12:30 Data e Horário
Março
4
Auditório1Conferência 8 (CF8)
Resumo:The presence of censoring occurs when data on the dependent variable is limited or lost. This paper extends the analysis of the seemingly unrelated regression (SUR) Tobit model for two right-censored dependent variables by modeling its nonlinear dependence structure through the rotated version of the Clayton copula.
The ability in capturing the upper tail dependence of the SUR Tobit model where data is censored is a useful feature of the modeling. We propose a modified version of the inference function for margins (IFM) method to obtain the estimates of the marginal and copula association parameters. Bootstrap methods are also proposed for obtaining confidence intervals for the model parameters. A simulation study is performed in order to examine the behavior of the new method estimates and check the coverage probability of the bootstrap confidence intervals in data sets with different sample sizes, percentages of censoring in the margins/dependent variables and degrees of dependence between them. The satisfactory results from the simulation and empirical studies indicate the good performance of our proposed model and methods. The methodology is applied to model the times to churn of customers on two credit products of a commercial bank. This work is co-authored by Paulo H. Ferreira.
Bivariate Rotated Clayton Copula-based SUR Tobit Right-Censored Model Francisco Louzada
ICMC-USP
11:30 ate 12:30 Data e Horário
Março
4
Auditório 2
Conferência 9 (CF9)
Resumo: The multivariate nonlinear mixed model (MNLMM) has been exploited as an effective tool for modelling multi-outcome longitudinal data following nonlinear growth patterns. In the framework of MNLMM, the random effects and within-subject errors are routinely assumed to be normally distributed for mathematical tractability and computational simplicity. However, a serious departure from normality may cause lack of robustness and subsequently make invalid inference. In this talk, I introduce a robust extension of the MNLMM by considering a joint multivariate t distribution for the random effects and within- subject errors, called the multivariate t nonlinear mixed model (MtNLMM).
Moreover, a damped exponential correlation structure is employed to capture the extra serial correlation among irregularly observed multiple repeated measures. An ECM procedure coupled with the first-order Taylor approximation is developed for estimating model parameters.
The techniques for estimation of random effects, imputation of missing responses and identification of potential outliers are also investigated. The methodology is applied to a real data example on 161 pregnant women coming from a study in a private fertilization obstetrics clinic in Santiago, Chile.
(Joint work with Dr. Wan-Lun Wang, Feng Chia University, Taiwan) Multivariate t nonlinear mixed models for multivariate longitudinal data with missing values
Tsung-I Lin
National Chung Hsing University, Taichung, Taiwan
14 0: 0 ate 1 : 05 0 Data e Horário
Março
4
Auditório 1
Conferência 10 (CF10)
Resumo:Os poderes locais dos testes da razão de verossimilhanças, Wald, escore e gradiente sob a presença de um vetor de parâmetros, ômega, que é ortogonal aos parâmetros restantes são considerados nesta apresentação. Será mostrado que alguns dos coeficientes que definem os poderes locais destes testes ficam inalterados independentemente se ômega é conhecido ou precisa ser estimado, enquanto que os outros coeficientes podem ser expressados como a soma de dois termos, o primeiro deles corresponde ao termo que é obtido como se ômega fosse conhecido, e o segundo, um termo adicional produzido pelo fato de ômega ser desconhecido. Esse resultado será aplicado na classe de modelos de regressão não lineares mistos e os poderes locais dos testes serão comparados.
Poder local dos testes da razão de verossimilhanças, Wald, escore e gradiente no modelo de regressão não linear misto
Artur José Lemonte
Universidade Federal de Pernambuco, Brasil
14 0: 0 ate 1 : 05 0 Data e Horário
Março
4
Auditório 2
Conferência 11 (CF11)
Resumo: We first review some common approaches to nonparametric Bayesian regression. We briefly review regression with nonparametric residual distribution, nonparametric mean function and fully nonparametric regression (density regression). We then focus on the latter and discuss in more detail a novel model for regression with a variable dimension parameter vector. The motivating application is subgroup analysis for a clinical trial of targeted therapy. The covariates are indicators of genetic aberrations, with each mutation only being recorded for a small subset of patients. We construct the desired regression model as a covariate- dependent random partition model, using for each patient only the available mutations.
Nonparametric Bayesian regression Peter Mueller
University of Texas, Austin, USA
15 0: 0 ate 1 : 06 0 Data e Horário
Março
4
Auditório 1
Conferência 12 (CF12)
Resumo: We present methods to assess undergraduate students’ performance.
Emphasis is mainly given to potential dissimilar behaviors due to high school background (Private or Public), but socioeconomic status and demographic characteristics may be used as well. Two analysis are presented: one based on a nonparametric method using measures of diversity and a decomposition of quasi U- statistics to define average distances between and within groups; and another based on generalized linear mixed models (GLMM). An advantage of the nonparametric method over the classical analysis of variance is its robustness to distributional deviation from the normality. Moreover, compared with other nonparametric methods, it also includes tests for interaction effects which are not rank transform procedures. Two data sets are analyzed, being both of them from the University of Campinas (Unicamp). The first one is formed by students who enrolled at Unicamp between 1997 and 2000 and their academic performance has been recorded until graduation or drop-out. The second data set is formed by students admitted to Unicamp from 2000 through 2005 and their academic performance and socioeconomic variables forms the study database. For each student we have the Entrance Exam Score (EES), the final Grade Point Average (GPA) score as well as the number of courses he/she failed during his/her Bachelor’s degree. The courses are separated in two categories: Required and Elective. Therefore, for the GPAscore and the number of courses failed, each student may have at most two measurements.
We model the GPA score and the incidence of courses failed for Required and Elective courses according to the EES, socioeconomic and demographic characteristics.
Joint work with Mariana R. Motta e Gabriel Franco.
Analysis of academic performance of students via quasi U-statistics and generalized linear mixed models
Hildete Prisco Pinheiro
Department of Statistics, University of Campinas, Brazil
15 0: 0 ate 1 : 06 0 Data e Horário
Março
4
Auditório 2
Conferência de Encerramento
Resumo: The construction of some wider families of continuous distributions obtained recently has attracted applied statisticians because the analytical facilities available for easy computation of special functions in programming softwares. In this talk, we outline some recent generating families of continuous distributions and discuss some of their properties. We review the beta, Kumaraswamy, gamma and T- X families of distributions. Some special cases, which are natural members of these families, are presented. Several known continuous distributions are found to be special cases of the current families. These properties are not difficult to be implemented in programming softwares such as R, MATHEMATICA and MAPLE.
Some examples illustrate the potentiality of the new models.
Extended Families of Continuos Distributions Gauss Cordeiro
DE–UFPE
11 2: 0 ate 1 : 02 3 Data e Horário
Março
5
Auditório 3
Resumo: In modern medicine one may be interested in predicting the stage occupation probabilities of different stages of the disease of a patient from high dimensional genomic and proteomic profiles. We introduce a method of constructing non-parametric regression estimates of state occupation probabilities in a multistate model.
In order to tackle a potentially large number of predictors in modern genomic and proteomic data sets we use partial least squares to compute estimated latent factors from the transition times along with the covariates which are then used in an additive model in order to avoid the curse of dimensionality. We illustrate the methodology using simulated and real data sets.
Nonparametric Regression and Partial Least Squares Dimension Reduction in Multistate Models
Susmita Datta University of Louisville,
Louisville, USA
Sessão Temática 1 ( ST 1.1): Big Data
Auditório 3
Data Março
3
Sessão Temática 1 ( ST 1.2): Big Data
Resumo: The quality of genotype calling for next-generation sequence data depends on read depth. Loci with high coverage can typically be reliably called, while those with low coverage may be difficult to call. In a case-control study, if data from case participants is sequenced to a greater depth than data from controls, the difference in genotype quality can introduce a systematic bias. This can easily occur when historical controls (e.g., data from the 1000 Genomes Project) are used. This imbalance may also occur by design, to reduce genotyping costs among controls.
For trios, bias can arise even when the coverage is the same in parents and offspring since errors in parental genotype calls are considered non-transmissions while errors in offspring genotype calls are detected as non-Mendelian transmissions.
Methods: We develop likelihood-based methods for analyzing data from case- control and trio studies that directly uses data on reads without first making intermediate genotype calls. When the location of polymorphic loci is known, we show these likelihood approaches have appropriate size and good power compared with methods that use called genotypes. When the locations of polymorphic loci are not known in advance, we develop screening methods to screen out loci that are estimated to be monomorphic, based on read data alone. We use a bootstrap approach to estimate which of the loci that screen in are truly polymorphic. Using these estimates, we then construct bootstrap tests for association that properly account for screening and preserve size. We further show that restricting to loci with estimated allele frequency≥1/2N, so that the expected number of alleles seen is greater than one, increases the power of our approach by excluding loci that have negligible effect.
Results: We illustrate our approach using data from the UK10K project. We use data from 784 cases from the Severe Childhood Onset Obesity Project, and are exome sequenced at 60x. Data for 1702 controls are from the Avon Longitudinal Study of Parents and Children and the TwinsUK study (only one twin used), and are whole genome sequenced at 6x coverage.
Testing Association without Calling Genotypes Allows for Systematic Differences in Read Depth and Sequencing Error Rate
Between Data from Case and Control Participants Glen Satten
Centers for Disease Control and Prevention, Atlanta, USA
Resumo: Even though a number of regression techniques have been proposed over the years to handle a large number of regressors, due to the complex nature of data emerging from recent high-throughput experiments, it is unlikely that any single technique will be successful in modeling all data types.
Thus, multiple regression algorithms from the collection of modern regression techniques that are capable of handling high dimensional regressors should be entertained for analyzing such data. A novel approach of building a super regression learner is proposed which can be fit with a training data set in order to make future predictions of a continuous outcome. The resulting super regression model is multi- objective in nature and mimics the performances of the best component regression models irrespective of the data type.
This is accomplished by combining elements of bootstrap based risk calculation, rank aggregation, and stacking. The utility of this approach is demonstrated through its use on mass spectrometry data.
Ensemble Regression Sommath Datta University of Louisville,
Louisville, USA
Sessão Temática 1 ( ST 1.3): Big Data
Auditório 3
Data Março
3
Sessão Temática 2 ( ST 2.1): Spatial Statistics
Resumo:Data that include fine geographic information, such as census tract or street block identifiers, can be difficult to release as public use files. Fine geography provides information that ill-intentioned data users can use to identify individuals.
We propose to release data with simulated geographies, so as to enable spatial analyses while reducing disclosure risks. We fit disease mapping models that predict areal-level counts from attributes in the file and sample new locations based on the estimated models.
We illustrate this approach using data on causes of death in North Carolina, including evaluations of the disclosure risks and analytic validity that can result from releasing synthetic geographies.
Imputation of confidential data sets with spatial locations using disease mapping models
Thais Paiva
Department of Statistical Science at Duke University, USA.
Auditório 3 Data
Março
4
Sessão Temática 2 ( ST 2.2): Spatial Statistics
Resumo: We develop a novel methodology for functional magnetic resonance imaging (fMRI) analysis based on a three components hemodynamic response function. Specifically, we propose a novel hemodynamic response function that is a mixture of three gamma densities. In addition, we use a Johnson-Rossell nonlocal prior to model the regression parameters associated to neuronal activation.
Further, to estimate the model parameters we develop a Markov chain Monte Carlo algorithm. Our hemodynamic response function is flexible enough to accommodate distinct physiological responses in different parts of the brain. We illustrate our methodology with the analysis of a single-subject fMRI visual task experiment.
An analysis of functional MRI with a three components hemodynamic response function
Marco Ferreira
Department of Statistics at Virginia Tech, USA
Auditório 3
Data Março
4
Sessão Temática 2 ( ST 2.3): Spatial Statistics
Resumo:Hidden structure in geo-referenced health data is now a focus of much research. There are a number of approaches to the modeling of such structure, ranging from classical random effect models to full latent variable modeling with geo-referencing. In this talk I will focus on two examples of recent latent variable model approaches. First I will consider the analysis of a spatially-dependent environmental predictor (PM2.5 in the counties of Georgia USA) and the use of latent space-time component mixtures in two stage model for health exposure risk.
Second, I will consider spatial survival modeling where we have a contextual spatial effect and discrete spatial changes in regression coefficients so that different area of the study region can have different relations to the health outcome.
In this approach discrete spatial prior distribution models must be considered and threshold CAR models are proposed as a simple approach. This is applied to prostate cancer cases from SEER registry data for the state of Louisiana USA(2000-2004).
Latent Structure modeling in Spatio-temporal small area Health data Andrew Lawson
Dept of Public Health Sciences, MUSC, USA
Auditório 3 Data
Março
4
Sessão Temática 3 ( ST 3.1): Non parametrics methods
Resumo: In this work we will discuss the use of wavelets in statistical methodologies that are based on Fourier decompositions. We briefly overview methods like classification, estimation based on biased data, additive regression and estimation of conditional densities. We focus on the problem of estimating regression functions of heteroscedastic models of the kind Y = f (X) + g(X), where is independent of X, with mean 0 and variance 1. We will emphasize the estimation of the probability function in mixture regression models.
Basically, there is a process Y that can be observed randomly in the time, say T, which is supported on the unit interval. For a fixed time T = t, such a process can be either a random variable (r.v.) V with probability f (t) or a r.v. W with probability 1 f (t), where V and W are assumed to have known and different means. We illustrate this method by numerical simulation studies for different probability functions f.
Key words: Wavelet estimation, nonparametric regression, mixture regression.
Waveletizing statistical procedures based on Fourier expansions Michel H. Montoril
University of Campinas, Brazil
Auditório 3
Data Março
5
Sessão Temática 3 ( ST 3.2): Non parametrics methods
Resumo:Extraction of a signal in the presence of stochastic noise via wavelet shrinkage has been studied under assumptions that the noise is independent and identically distributed (IID) and that the samples are equispaced (evenly spaced in time). Previous work has relaxed these assumptions either to allow for correlated observations or to allow for random sampling, but very few papers have relaxed both together.
In this paper we relax both assumptions by assuming the noise to be a stationary Gaussian process and by assuming a random sampling scheme dictated either by a uniform distribution or by an evenly spaced design subject to jittering. We show that, if the data are treated as if they were autocorrelated and equispaced, the resulting wavelet-based shrinkage estimator achieves an almost optimal convergence rate. We investigate the efficacy of the proposed methodology via simulation studies and extraction of the light curve for a variable star.
Wavelet Shrinkage for Regression Models with Random Design and Correlated Errors
Rogério F. Porto Banco do Brasil
Auditório 3 Data
Março
5
Sessão Temática 3 ( ST 3.3): Non parametrics methods
Resumo:Calibration and prediction for NIR spectroscopy data are performed based on a functional interpretation of the Beer-Lambert formula. Considering that, for each chemical sample, the resulting spectrum is a continuous curve obtained as the summation of overlapped absorption spectra from each analyte plus a Gaussian error, we assume that each individual spectrum can be expanded as a linear combination of B-splines basis. Calibration is then performed using two procedures for estimating the individual analytes curves: basis smoothing and smoothing splines.
Prediction is done by minimizing the square error of prediction. To assess the variance of the predicted values, we use a leave-one-out jackknife technique.
Departures from the standard error models are discussed through a simulation study, in particular, how correlated errors impact on the calibration step and consequently on the analytes’ concentration prediction. Finally, the performance of our methodology is demonstrated through the analysis of two publicly available datasets.
Key words: B-splines, leave-one-out jackknife, square error of prediction Aggregated functional data model for Near-Infrared
Spectroscopy calibration and prediction Ronaldo Dias
University of Campinas Brazil
Auditório 3
Data Março
5
Comunicação Oral 1.1 ( CO 1)
Resumo: En este trabajo discutimos inferencia estadística y diagnósticos de influencia en un modelo estadístico usado para comparar instrumentos de medición en presencia de un gold estándar. Suponemos que las mediciones de los instrumentos siguen una distribución normal multivariada. Consideramos test de hipótesis y regiones de confianza para parámetros de interés, e implementamos el método de influencia local para analizar la sensibilidad de los estimadores máximo verosímiles a perturbaciones del modelo estadístico y/o de los datos. Finalmente ilustramos la metodología con datos reales.
Palavras-Chave:Inferencia estadística, Diagnósticos de influencia, Comparación de métodos de medición, Gold estándar
Comparación de métodos de medición en presencia de un gold estándar Manuel Galea
Auditório 1 Data
Março
3
Comunicação Oral 1.2 ( CO 1)
Resumo: In regression analysis, when the covariates are not exactly observed, measurement error models extend the usual regression models toward a more realistic representation of the covariates. In a recent contribution, Wang &
Sivaganesan (2013) propose objective priors for the parameters in normal measurement error models. The prior distributions are specified for the parameters in the regression model. Posterior inference requires MCMC computations. In our approach, the regression model is seen as a reparameterization of the bivariate normal distribution. We adapt the general results for objetive Bayesian inference in Berger & Sun (2008) to the regression framework. MCMC methods are not necessary at all.
Palavras-Chave:Acceptance-rejection, estimation, MCMC methods, regression models, simulation.
Objective Bayesian inference in measurement error models Mário de Castro
Ignacio Vidal
Auditório 1
Data Março
3
Comunicação Oral 1.3 ( CO 1)
Resumo: Em análise de regressão, a análise de diagnóstico tem como papel principal averiguar a qualidade do ajuste do modelo. Esta verificação pode ser feita tanto através de análise de resíduos, que detecta a presença de pontos extremos e avalia se a distribuição proposta para a variável resposta está adequada quanto via análise de influência local proposta por Cook [1986]. Na análise de influência local, Cox and Snell [1986] discutem um método que avalia a influência de perturbações no modelo de regressão, por menor que seja esse fator de perturbação. Na literatura, há diversos trabalhos envolvendo análise de diagnósticos. Para o modelo de regressão beta, podemos citar as seguintes referências: Ferrari and Cribari-Neto [2004], Espinheira et al. [2008a], Espinheira et al. [2008b], Ferrari et al. [2011] e Carrasco et al. [2014]. Enquanto que para modelos com erro de medida, temos:
Kelly [1984], Miller [1990], Carroll and Spiegelman [1992], Zhao et al. [1994], Zhao and Lee [1995] e Xiea and Bo-ChengWei [2009]. Carrasco et al. [2014]
realizaram uma análise de resíduos para o modelo de regressão beta com erro de medida aditivo. Neste trabalho apresentamos as principais técnicas de diagnósticos construídas para o modelo de regressão beta considerando erro de medida multiplicativo.
Palavras-Chave:Modelos com erros nas covariáveis, Regressão Beta e Análise de Diagnósticos.
Análise de Diagnósticos para o Modelo de Regressão Beta com Erro de Medida Multiplicativo
Eveliny Barroso da Silva Carlos Alberto Ribeiro Diniz Jalmar Manuel Farfan Carrasco
Auditório 1 Data
Março
3
Comunicação Oral 2.1 ( CO 2)
Resumo:Problemas envolvendo dados de contagem podem resultar em conjunto de dados com uma grande quantidade de zeros. Quando utilizamos distribuições usuais (Poisson, Binomial ou Binomial Negativa) em conjuntos com excesso de zeros, análises estatísticas podem apresentar-se errôneas. As distribuições mais indicadas para este caso são as compostas por uma mistura de distribuições, sendo uma com massa no ponto zero e outra que se adequaria aos dados caso não houvesse a inflação de zeros.
Neste artigo, utilizamos a distribuição Binomial bivariada inflacionada de zeros como base para a construção do modelo de regressão binomial bivariado inflacionado de zeros com estrutura de correlação autoregressiva nos componentes aleatórios do modelo, também conhecido como modelo de regressão autoregressivo binomial bivariado inflacionado de zeros. A metodologia BLUP é utilizada no processo de maximização dos efeitos fixos (parâmetros) e efeitos aleatórios. A parte computacional deste trabalho foi realizada em linguagem Ox.
Palavras-Chave: Modelos zero-inflacionados, binomial bivariada, efeito aleatório.
Modelo de regressão bivariado inflacionado de zeros com estrutura de correlação autoregressiva de primeira
ordem nos componentes aleatórios Natália Manduca Ferreira Carlos Alberto Ribeiro Diniz
Auditório 2
Data Março
3
Comunicação Oral 2.2 ( CO 2)
Resumo:In this paper, we propose the use of Bayesian quantile regression for the analysis of proportion data. We also consider the case when the data presents a zero or one inflation using a two-part model approach. For the latter scheme, we assume that the response variable is generated by a mixed discrete-continuous distribution with a point mass at zero or one. Quantile regression is then used to explain the conditional distribution of the continuous part between zero and one, while the mixture probability is also modeled as a function of the covariates. We check the performance of these models with two simulation studies. We illustrate the method with data about the proportion of households with access to electricity in Brazil.
Palavras-Chave: Bayesian quantile regression; proportion data; two-part model;
proportion of households with access to electricity in Brazil.
Bayesian analysis for zero-or-one inflated proportion data using quantile regression
Bruno Santos Heleno Bolfarine
Auditório 2 Data
Março
3
Comunicação Oral 2.3 ( CO 2)
Resumo:In this paper we propose the zero-inflated COM-Poisson distribution. We develop a Bayesian analysis for our approach based on Markov chain Monte Carlo methods. We discuss regression modeling and model selection, as well as, develop case deletion influence diagnostics for the joint posterior distribution based on the ψ-divergence, which has several divergence measures as particular cases, such as the Kullback-Leibler (K-L), J-distance, L1 norm and χ2 -square divergence measures. The performance of our approach is illustrated in an artificial dataset as well as in a real dataset on an apple cultivar experiment.
Palavras-Chave: Bayesian Inference, COM–Poisson Distribution, Kullback- Leibler Distance, Zero-Inflated Models.
The Zero-inflated Conway-Maxwell Poisson Model to Analyze Discrete Data Gladys D. C. Barriga
Francisco Louzada Vicente G. Cancho
Auditório 2
Data Março
3
Comunicação Oral 3.1 ( CO 3)
Resumo:The aim of our research is to provide algorithms of data imputation for a cyclostationary time series with heavy tails. We assume that time series of interest is K-dependent but also has heavy tails. We use the multivariate t distribution with the covariance matrixΣof order 2 (K−1) × 2 (K−1). Moreover, we assume that the number of degrees of freedomνis fixed and 2 <ν ≤6. We use the periodic sequence {ct} with the period H as the periodic amplitude imposed over the stationary background time series. We propose four imputation algorithms based on the properties of the multivariate t-distribution. Using simulations, we compare the performance of those algorithm.
Imputation of missing observations for heavy tailed cyclostationary time series
Christiana Drake Jacek Leskow Aldo M. Garay Victor H. Lachos
Auditório 3 Data
Março
3
Comunicação Oral 3.2 ( CO 3)
Resumo:We developed a space-time prospective surveillance method when the data are point events, monitoring if there is an emerging cluster. Typical application areas are crime or disease surveillance. At each new event, a local Knox score is calculated and spatially spread to form a stochastic surface. The surfaces are accumulated sequentially until they exceed a specified threshold, causing an alarm to go off and identify the region of the probable cluster. The method requires little prior knowledge from the user and provides a way to identify locations and time of possible clusters, through the visualization of the cumulative surface. We present a simulation study for different cluster scenarios, as well as an application to a dataset of meningitis cases in Belo Horizonte, Brazil.
Palavras-Chave:Spatial Statistics, Disease Mapping, Surveillance, Point Patter, Space-Time, Local Knox Score, Cumulative Surfaces.
Prospective space-time surveillance with geographical identification of the emerging cluster
Thais V. Paiva Renato M. Assunção
Taynana C. Simões
Auditório 3
Data Março
3
Comunicação Oral 3.3 ( CO 3)
Resumo: O presente trabalho propõe melhoramentos inferenciais em pequenas amostras para o modelo beta autorregressivo de médias móveis (βARMA). O modeloβARMA é útil para modelar e prever variáveis contínuas pertencentes ao intervalo (0,1), como taxas e proporções.
Os procedimentos inferenciais baseados nos estimadores de máxima verossimilhança possuem boas propriedades assintóticas, mas em pequenas amostras podem ter desempenho pobre. Neste sentido, são propostas correções bootstrap dos estimadores pontuais, assim como diversas abordagens bootstrap são consideradas para melhoramentos dos intervalos de confiança. Tais correções são avaliadas numericamente via um extensivo estudo de simulações de Monte Carlo.
Os resultados numéricos evidenciam que as inferenciais em amostras de tamanho baseadas nas correções bootstrap propostas são mais confiáveis do que quando considerados os estimadores de máxima verossimilhança usuais. Uma aplicação a dados reais mostra que os valores previstos da variável de interesse são mais fidedignos quando os estimadores corrigidos são considerados.
Melhoramentos inferenciais via bootstrap no modelo beta autorregressivo de médias móveis
Bruna Gregory Palm Fábio M. Bayer
Auditório 3 Data
Março
3
Comunicação Oral 4.1 ( CO 4)
Resumo:The bivariate Sinh-Elliptical (BSE) distribution is a generalization of the well-known Rieck’s (Ph.D. thesis, Department of Mathematical Sciences, Clemson University, USA, 1989) Sinh-Normal distribution that is quite useful in Birbaum Saunders (BS) regression model. The main aim of this paper is to define the BSE distribution and discuss some of its properties, such as marginal and conditional distributions and moments. In addition, the asymptotic properties of method of moments estimators are studied, extending some existing theoretical results in the literature.
These results are obtained by using some known properties of the bivariate elliptical distribution. This development can be viewed as a follow-up to the recent work on bivariate Birnbaum-Saunders distribution by Kundu et al. (J. Mult. Anal. 101: 113- 125, 2010) towards some applications in the regression setup. The measurement error models are also introduced as part of the application of the results developed here. Finally, numerical examples using both simulated and real data are analyzed, illustrating the usefulness of the proposed methodology.
Palavras-Chave: Sinh-Normal distribution; Elliptical distribution; Kurtosis;
Moment estimators; Consistent estimators; Asymptotic properties; Regression models; Measurement error models.
The Bivariate Sinh-Elliptical Distribution with Applications to Birnbaum-Saunders Distribution and
Associated Regression and Measurement Error Models Filidor Vilca
N. Balakrishnan Camila Borelli Zeller
Auditório 3
Data Março
4
Comunicação Oral 4.2 ( CO 4)
Resumo: We propose a methodology based on a reparameterized Birnbaum- Saunders regression model with varying precision, which generalizes the existing works in the literature on the topic. This methodology includes the estimation of model parameters, hypothesis tests for the precision parameter, a residual analysis and influence diagnostic tools. Simulation studies are conducted to evaluate its performance. We apply it to a real-world case-study to show its potential.
Palavras-Chave: Birnbaum-Saunders distribution; hypothesis testing; local influence; maximum likelihood method; Monte Carlo simulation; residuals.
Reparameterized Birnbaum-Saunders regression models with varying precision
Manoel Santos-Neto Francisco Jose A. Cysneiros
Víctor Leiva Michelli Barros
Auditório 3 Data
Março
4
Comunicação Oral 4.3 ( CO 4)
Resumo: Este trabalho aborda metodologias de estimação e diagnóstico em modelos de regressão baseados na distribuição Birnbaum-Saunders com erros de medidas aditivo e multiplicativo. Técnicas de estimação como máxima pseudo- verossimilhança e calibração da regressão são utilizadas. Também são abordados, medidas como análise de resíduos, influência global e local. Um conjunto de dados numéricos são utilizados, com o intuito de validar os resultados obtidos.
Palavras-Chave:Distribuição Birnbaum-Saunders; erros de medida, regressão, diagnóstico
Inferência e diagnóstico em modelos com
erros nas variáveis baseado na distribuição Birnbaum-Saunders Jalmar M. F. Carrasco
Jorge I Figueroa-Zuniga Victor L. P. Leiva Marco A. R. Álamos
Auditório 3
Data Março
4
Comunicação Oral 5.1 ( CO 5)
Resumo:In this paper we present a novel methodology to perform Bayesian model selection in linear models with heavy-tailed distributions. The new method considers a finite mixture of distributions to model a latent variable where each component of the mixture corresponds to one possible model within the symmetrical class of normal independent distributions. Naturally, the Gaussian model is one of the possibilities. This allows a simultaneous analysis based on the posterior probability of each model. Inference is performed via Markov chain Monte Carlo - a Gibbs sampler with Metropolis–Hastings steps for a class of parameters. Simulated studies highlight the advantages of this approach compared to a segregated analysis based on arbitrary model selection criteria.An example with real data is also presented.
Palavras-Chave: Finite mixture; heavy-tailed errors; linear models; model selection; MCMC.
Robust Bayesian model selection for heavy-tailed linear regression models using finite mixtures
Flávio B. Gonçalves Marcos O. Prates Victor H. Lachos
Auditório 1 Data
Março
5
Comunicação Oral 5.2 ( CO 5)
Resumo:This work proposes a general Bayesian semi-parametric model to binary data. It is considered symmetric prior probability curves as an extension for discussed ideas from [4] using the Blocked Gibbs sampler which is more general than the Polya Urn Gibbs sampler. The semi-parametric approach allows to incorporate the uncertainty around the F distribution of the latent data and modeling heavy-tailed or light-tailed distributions than that prior proposed. In particular, the Bayesian semi-parametric Logistic model is introduced which enables one to elicit prior distributions for regression coefficients from information about odds ratios what is quite interesting in applied research. Then, this framework opens several possibilities to deal with binary data in the Bayesian perspective.
Bayesian semi-parametric symmetric models for binary data Marcio Augusto Diniz
Carlos Alberto de Braganca Pereira Adriano Polpo
Auditório 1
Data Março
5
Comunicação Oral 5.3 ( CO 5)
Resumo:Neste trabalho estudamos a inferência estatística sob o enfoque Bayesiano nos modelos semiparamétricos com erros nas variáveis, em que seu componente sistemático admite variáveis explicativas com e sem erro de medição, bem como a presença de um efeito não-linear aproximado através de um B-spline (veja, por exemplo, De Boor (1978)). Nestes modelos, o componente aleatório do modelo considera distribuições com caudas mais pesadas do que a distribuição normal multivariada, este componente é descrito usando vetores aleatórios obtidos como misturas na escala da distribuição normal multivariada (veja, por exemplo,Andrews e Mallows, 1974), o qual proporciona flexibilidade bem como robustez frente a observações extremas na modelagem. Como exemplos desta classe podemos citar as distribuições multivariadas t-Student, slash, Laplace, hiperbólica simétrica e normal contaminada. Para obter amostras da distribuição a posteriori dos parâmetros do modelo propomos um algoritmo MCMC. O comportamento do algoritmo é avaliado através de um estudo de simulação. A proposta metodológica é aplicada a um conjunto de dados reais, no qual podemos observar que ignorar os erros de medição pode levar a obter conclusões erradas. Além disso, a função fmem() do pacote BayesGESM (http://cran.r-project.org/package=BayesGESM) no R (www.r-project.org) é apresentada, esta função fornece uma maneira fácil de aplicar a metodologia apresentada neste trabalho.
Palavras-Chave: Inferência Bayesiana, modelos com erros nas variáveis, modelos semiparametricos, algoritmo MCMC, B-splines, mistura na escala da distribuição normal.
Inferência bayesiana em modelos semiparamêtricos com erros nas variáveis Luz Marina Rondón Poveda
Heleno Bolfarine
Auditório 1 Data
Março
5
Comunicação Oral 6.1 ( CO 6)
Resumo:We present a class of randomly truncated nonlinear beta mixed-effects models where the truncated nature of the data is incorporated into the statistical model by considering the truncation limits to be random variables and by assuming the variable of interest to follows a truncated beta distribution parametrized by a mean and a dispersion parameter. The location parameter of the responses is associated with a nonlinear continuous function of covariates and unknown parameters and with unobserved random effects. Maximum likelihood estimator of the parameters are obtained by direct maximization of the log-likelihood function via an iterative procedure and diagnostic analysis tools are considered to check for model adequacy. A data sets consisting of observations on soil-water retention from a soil profiles from the Buriti Vermelho River Basin database is analyzed using the proposed methodology.
Palavras-Chave: Truncated beta distribution, random truncation, nonlinear mixedeffects model, iterative maximum likelihood, diagnostic analysis, soil-water retention.
Randomly truncated nonlinear beta mixed-effects models Carolina Costa Mota Paraíba
Carlos Alberto Ribeiro Diniz
Auditório 2
Data Março
5
Comunicação Oral 6.2 ( CO 6)
Resumo:We propose in this paper a random intercept gamma model in which the random effect is assumed to follow a generalized log-gamma (GLG) distribution.
This flexibilization in which has been suggested by Fabio et al (2012) allows distributions for the random effect skew to the right and skew to the left and has the normal distribution as a particular case. For a particular parametrization for the GLG distribution and specifying the adequate link function, we derive a new continuous multivariate distribution called Gamma-GLG . Then, we obtain the moments this joint density function and a Newton Raphson iterative process was developed for obtaining the maximum likelihood estimates for the parameters of the multivariate model. Two desviance functions and residuals analysis are proposed and an applications with real data is given for illustration.
Palavras-Chave: Generalized linear models; Random-effect models; Generalized log-gamma distribution; Residual analysis; Gamma-GLG distribution.
The multivariate Gamma-GLG model from the random intercept Gamma model with random effect nonnormal
Lizandra C. Fabio Francisco J.A. Cysneiros
Gilberto A. Paula
Auditório 2 Data
Março
5
Comunicação Oral 6.3 ( CO 6)
Resumo:We propose a data-driven reversible jump to QTL mapping in which the phenotypic trait is modeled as a linear function of the additive and dominance effects of the unknown QTL genotypes. We also present and compare different methods to update the QTLs location and check the performance of the methodologies on simulated and real data-sets. We observe that the data-driven proposals improved the acceptance probability of dimensional change moves of reversible jump and, consequently, its convergence and increase the exploration of model space.
Palavras-Chave: QTL mapping; data-driven reversible jump; update of parameters block.
Data-driven reversible jump to QTL mapping Daiane Aparecida Zuanetti
Luís Aparecido Milan
Auditório 2
Data Março
5
Comunicação Oral 7.1 ( CO 7)
Resumo: A multidimensional item response theory model with latent linear structure for several groups is proposed. This model was introduced in order to fit binary tests, which in turn are divided in several subtest and subsequently applied to different groups or populations. It is assume that each subtest measure a one- dimensional latent trait (main latent trait or main ability). The main aim is to measure these latent traits. Furthermore, it is also assumed that the entire test measures a latent trait vector from tested subjects. This latent trait vector does not necessary have the same components as the main latent trait. Instead, it is supposed that the main latent traits are linear combinations of latent trait vector components.
Therefore, they have a linear latent structure. Each item is assumed to belong to exactly one subtest. In this model, the test dimension is defined as the number of subtest and it may not equal the latent trait space dimension. In order to estimate the parameters, an augment data Gibbs sampler (DAGS) was implemented and tested in simulations. Besides, the model was used to fit data from the’First comparative survey on language, math and associated factors for 3rd and 4th year students (PERCE)’, which was carried out by the Latinamerican laboratory for assessment of quality of education.
Palavras-Chave: Teoria da resposta ao item multidimensional, estrutura linear latente, vários grupos, subteste, traço latente.
Modelo LSMIRT para varias populações Gualberto S.A. Montalvo
Auditório 1 Data
Março
5
Comunicação Oral 7.2 ( CO 7)
Resumo:Providing a new distribution is always precious for statisticians. A new three-parameter distribution called the odd log-logistic normal (OLLN) distribution is defined and studied. Various of its structural properties are derived including some explicit expressions for the moments, generating functions, mean deviations and incomplete moments. Maximum likelihood techniques are used to t the new model and to show its potentiality by means of three the real data sets in analysis of experiments. Based on three criteria, the proposed distribution provides a better t then the normal, skew normal, beta normal, Kumaraswamy normal and gamma normal distributions.
Palavras-Chave:Log-logistic distribution; Maximum likelihood estimation; Mean deviation; Normal distribution.
The odd log-logistic normal distribution:
theory and applications in analysis of experiments Altemir da Silva Braga
Gauss M. Cordeiro Edwin M. M. Ortega
José Nilton da Cruz
Auditório 1
Data Março
5
Comunicação Oral 7.3 ( CO 7)
Resumo:In survival analysis applications, the failure rate function may frequently present a unimodal shape. In such case, the log-normal and log-logistic distributions are used. In this paper, we shall be concerned only with parametric forms, so a location-scale regression model based on the odd log-logistic Weibull distribution is proposed for modeling data with a decreasing, increasing, unimodal and bathtub failure rate function as an alternative to the log-Weibull regression model. For censored data, we consider a classic method to estimate the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes and censoring percentages, various simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We analyze a real data set using the log-odd log-logistic Weibull regression model.
The log-odd log-logistic Weibull regression model:
modeling, estimation, influence diagnostics and residual analysis José Nilton da Cruz
Edwin M. M. Ortega Gauss M. Cordeiro
Ana K. Campelo
Auditório 1 Data
Março
