Missing data
Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF
... retrieve missing data, can result in an over-fitting of the EOFs to the few present ...Satellite data sets typically used for reconstruction with DINEOF may contain some im- ages with the ...
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Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa.
... of missing data is superior to the overlooking of the missing data, that the indicator method often provides biased results, that conditional mean imputation is better than unconditional ...
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Key words : Singular value decomposition, weights, missing data, genotype-by-
... with missing data in multi-environmental trials with genotype-by-environment interaction, based on the imputation system developed by Krzanowski that uses the singular value decomposition (SVD) of a ...real ...
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Missing Data in Time Series: Analysis, Model and Software Application
... TSCS data, which are the smoothing behaviour that time series present and the possibly sharp changes in space, due to the cross-section structure, can bring the imputations to be far from the real ...the ...
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Influence of Pattern of Missing Data on Performance of Imputation Methods: An Example from National Data on Drug Injection in Prisons
... whole data were served as gold standard. Missing data were then generated so that 10%, 20% and 50% of data were ...generated missing values, at above rates, in one variable which was ...
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A Novel Nonparametric Multiple Imputation Algorithm for Estimating Missing Data
... imputing missing values, while MS is the most widely used imputation ...remove data using MCAR and MAR mechanisms at different rates of missing values into the training ...imputation. Missing ...
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An alternative methodology for imputing missing data in trials with genotype-by-environment interaction
... of missing data, the loss of information produces unbalan- ced designs that lose their symmetry and, for instance, hypothesis tests of interest such as those for the difference between the treatments may ...
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Imputation methods for filling missing data in urban air pollution data for Malaysia
... Four imputation methods were used to estimate the all percentages of simulated missing data. The methods used are Mean Top Bottom (MTB), Nearest Neighbor (NN), Linear Regression (LR) and Multiple Imputation ...
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A framework to Deal with Missing Data in Data Sets
... some missing values due to unavailable data. Missing values minimizing the quality of classification rules generated by a data mining ...system. Missing vales also affecting the ...
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Novel Approach to Estimate Missing Data Using Spatio-Temporal Estimation Method
... of data in ...of missing block is estimated using spatial smoothening and the details (high frequency) are added using bidirectional (temporal) predication of high frequency wavelet ...of missing ...
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Exploratory factor analysis and reliability analysis with missing data: A simple method for SPSS users
... the Missing Vvlues Anvlysis vdd-on module, includes four methods for devling with missing vvlues: listwise vnd pvirwise deletion, single imputvtion viv regression, vnd expectvtion mvximizvtion ...
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Missing data (e.g. accounting figures)
... OfcF/BV_of Assets is calculated as net income plus depreciation & amortization plus other noncash adjustments plus changes in non- cash working capital divided by debt plus book val[r] ...
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Virologic and immunologic effectiveness of darunavir-based salvage therapy in HIV-1-infected adults in a Brazilian clinical practice setting: results of a multicenter and retrospective cohort study
... handling missing data, higher baseline viral load, higher viral load at anytime, lower nadir CD4 cell count, and lower number of previous failing ARTs were independently associated with virologic failure ...
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Técnicas de indexação de grandes conjuntos de dados complexos com valores de atributos...
... MCAR data is the loss of statistical ...complete data is equivalent to a simple random sample, and it is well-known that simple random sampling does not cause bias ...If data are MAR but not MCAR, ...
138
Rev. Bras. Psiquiatr.
... The High Risk Cohort Study for the Development of Childhood Psychiatric Disorders (HRC) is a large com- munity school-based study of children aged 6 to 14 years from 57 schools in two Brazilian cities: Porto Alegre ...
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Artificial Immune Systems and Missing Features Classification Approach
... Fig.1. Missing values percentages versus ...a missing attribute value presence and handling in used datasets could be effectively realized in combination with an artificial immune system ...for ...
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Association of Adiposity Indices with Platelet Distribution Width and Mean Platelet Volume in Chinese Adults.
... Hypoxia is a prominent characteristic of inflammatory tissue lesions. It can affect platelet function. While mean platelet volume (MPV) and platelet distribution width (PDW) are sam- ple platelet indices, they may ...
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Multiple imputation of missing categorical data using latent class models: State of the art
... where missing values are imputed (variable by variable) based on the current regression estimates for dependent variable ...that missing data draws converge to the posterior distribution of the ...
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Improving Accuracy and Coverage of Data Mining Systems that are Built from Noisy Datasets: A New Model
... be missing both for examples (tuples) in the training set and for examples which are to be classified [9] .... Missing data might occur because the value is not relevant to a particular case, could ...
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Improving KNN Method Based on Reduced Relational Grade for Microarray Missing Values Imputation
... Many data analysis methods, such as principal component analysis (PCA), singular value decomposition (SVD) and hierarchical clustering, just can be applied with complete datasets without missing ...that ...
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