In the Table 1. there are presented most popular kernel functions (I is an indicator function). As we can see only one of them, the Gaussian, has the infinite domain what means that it takes into consideration (estimation of the value at x) all given points, even very distant. Other kernels narrow the neighbourhood of x to the value of the smoothing parameter h.In the onedimensional case only pairs from the training set, which first entries belong to the **interval** [ − ℎ, + ℎ] are averaged.

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Following the seminar paper of Lucas (1978), a large theoretical literature has been de- voted to develop (C)CAPM models with different and fully specified endowment process and preferences to replicate a large set of empirical asset pricing facts (the high equity premium, the low risk free rate, the predictability of the price–dividend ratio and the shape of the real and nominal term structure curves...). Typically, the (C)CAPM models proposed in the literature differ by (i) the specification of preferences, (ii) the specification of the state variables, (iii) the **interval** decision of the consumer or/and (iv ) the sampling **interval** at which the model is empirically evaluated. Kocherlakota (1996), Campbell (2003) and Cochrane (2007) provide a thorough survey of this literature. For instance, Campbell and Cochrane (1999) solved for equilibrium asset prices in a (C)CAPM with habit formation and when the endowment growth rate follows an i.i.d process. They evaluated the model at monthly frequency and then conducted annual asset pricing mo- ments. Wachter (2006) applied the Campbell and Cochrane (1999) model to access its term structure implications. However, the model was evaluated at quarterly frequency. Piazzesi and Schneider (2006) provided solutions for the equilibrium yield curves under the assumptions of recursive preferences and a state-space specifications of both consump- tion growth rate and inflation. The model was evaluated at quarterly frequency. Bansal and Yaron (2004) developed a long–run risks (LRR) asset pricing model with recursive preferences and when the consumption growth rate presents both predictable and uncer- tainty components. They calibrated the model at monthly frequency. In Bansal, Kiku and Yaron (2010), the long–run risks model was estimated using annual financial and macroeconomic data and assuming that the decision **interval** of the consumer is monthly. They also evaluated empirically the effects of ignoring the time–aggregation if the decision **interval** was annual.

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R1-R2 = year-1 to -2 resident; R3-R4 = year-3 to -4 resident; ASA = American Society of Anesthesiologist physical status; bronchoscopy = ﬁberoptic- assisted tracheal intubation or examination of double lumen endobronchial tube. The numbers in boldface are estimated AIT based **on** results of the linear **regression** analysis and retransformed into the original scale with Duan’s smearing factor. Only conditional mean estimates of AIT can be obtained using the linear **regression** analysis. In contrast, it is possible to estimate anesthetic induction time under miscellaneous conditions at distinct quantiles using the quantile **regression** analysis. For example, if a simple general or regional anesthesia without any invasive procedures for a patient with ASA < III was completed by an R1-R2 resident in 20 minutes, we know that he or she ﬁnished this task slower than at least 75% of his or her peers (19 minutes at the quantile of 0.75). Similarly, AIT for various combinations of anesthetic procedures at distinct quantiles can also be estimated using this analytical approach. Note that the predicted mean AIT from the linear **regression** analysis are greater their median counterparts from the quantile **regression** analysis. This implies the right-skewed property of AIT distribution and quantile **regression** analysis can provide more comprehensive information throughout the whole distribution of AIT under miscellaneous conditions.

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Various classical optimization techniques are there based **on** what is to be optimised. **On** the basis of process parameters, optimization method is classified into two principal categories such as (a) Single-Variable Optimization and (b) Multi- Variable Optimization. Single Variable optimisation can be carried out by any of three methods namely, Bracketing methods, Region-Elimination methods and Gradient-based methods. **Interval** halving method is one of three sub categories of Region-Elimination methods.Because of complex nature of fluidized bed granulation and complexity of multivariable effects, single variable optimisation technique has been used in the present work for parameter optimization. In this work **Interval** Halving method has been adopted for optimisation of process parameters. The minimum of a function is found in two phases. First, a crude technique is used to find a lower and an upper bound of the minimum. Thereafter, a more sophisticated method is used to search within these limits and find the optimal solution with the desired accuracy. Before optimization of process parameters it is essential to express the output as a function of input parameters. Therefore attempt has been made to correlate the output of granulation process to all the system parameters in the present work by **regression** analysis.

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The above described geomorphological setting determines a significant risk of flooding for the human settlements lo- cated downvalley (CNR, 1993), mainly around the city of Pineto which is located **on** the river mouth. In July 1999 a severe flood occurred that submerged the coastal area with heavy damages to structures, roads, buildings and a high number of evacuees. Immediately after that catastrophic event, the local Authority commissioned a study with the aim to design precautionary measures and river engineering works to reduce the flood risk. The triggering factor of the flood that occurred in 1999 was recognised as the accumu- lation along the main stream of the Calvano River of sedi- ments supplied from the tributaries. The sediment yield was favoured by the low bedrock permeability, the high water- shed slope and the fine texture of sediments (Commissione Tecnica, 1999).

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In this paper we study the Buckley-James estimator of accelerated failure time models with auxiliary covariates. Instead of postulating distributional assumptions **on** the auxiliary covariates, we use a local polynomial approximation method to accommodate them into the Buckley-James **estimating** equations. The **regression** parameters are obtained iteratively by minimizing a consecutive distance of the estimates. Asymptotic properties of the proposed estimator are investigated. Simulation studies show that the efficiency gain of using auxiliary information is remarkable when compared to just using the validation sample. The method is applied to the PBC data from the Mayo Clinic trial in primary biliary cirrhosis as an illustration.

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Descriptive analyses were performed for all variables, with calculation of prevalence and confidence intervals (95% CI) for the categorical variables; and means and standard deviation for the continuous variables, stratified by gender. The chi-square test (categorical variables) and t-Stu- dent test (continuous variables) were used for the bivariate analysis. For the bivariate and adjusted analyses, linear **regression** was used **estimating** crude and adjusted ( ) coefficients, with their re- spective confidence intervals (95% CI). Three fit models were considered in the association for each chronic disease and HS: Model 1) adjusted for age, schooling and household arrangement; Model 2) adjusted for age, schooling and house- hold arrangement, smoking, physical activity, body mass index, functional disability and cogni- tive status; Model 3) adjusted for age, schooling and household arrangement, smoking, physical activity, body mass index, functional disability, cognitive status and for all chronic health con- ditions (systemic arterial hypertension, diabetes mellitus, cancer, chronic lung disease [asthma, bronchitis, emphysema], coronary disease, chron- ic renal failure, cerebrovascular disease [embo- lism, stroke, ischemia, cerebral thrombosis], ar- thritis / rheumatism / arthrosis, depression and falls) to eliminate the possible confounding effect of the presence of multiple chronic diseases. A lev- el of statistical significance of 5% was considered. The HS trend was analyzed for each gender by the number of morbidities. For this, HS predic- tive mean was calculated from a linear **regression** model, adjusted for age, schooling, household ar- rangement, smoking, physical activity, body mass index, functional disability and cognitive status. The significant difference was verified from the 95% confidence **interval**.

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The medians for all **regression** techniques applied **on** Finnish _Cluster are very close to zero, as it is clear from the values **on** the Y-axis, indicating that the estimates were closer to the minimum value. Outliers are less extreme in case of Finnish_Cluster. One sample Wilcoxon signed rank test has been applied in order to investigate the significance of the results by setting level of confidence to 0.05. From the results obtained as shown in Table 6, we can conclude that no significant difference exists between the residual median and hypothetical median.

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Among the commonly used methods, the non-destructive indirect one can be an alternative for **estimating** leaf area based **on** the **regression** relationship, using dimensions of the leaf such as length and width. This method has been used in various crops such as faba bean (Peksen 2007), potato (Silva et al. 2008a), cotton (Fideles Filho et al. 2010), sunflower (Aquino et al. 2011) and soybean (Richter 2014), allowing the continuity of the assessments in the same plant during its development cycle. In addition, it is a fast, accurate (Bianco et al. 2008) and low-cost technique.

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ABSTRACT: Plant growth analyses are important because they generate information **on** the demand and necessary care for each develop- ment stage of a plant. Nonlinear **regression** models are appropriate for the description of curves of growth, since they include parameters with practical biological interpretation. However, these models present information in terms of the conditional mean, and they are subject to problems in the adjustment caused by possible outliers or asymmetry in the distribution of the data. Quantile **regression** can solve these problems, and it allows the estimation of different quantiles, generating more complete and robust results. The objective of this research was to adjust a nonlinear quantile **regression** model for the study of dry matter accumulation in garlic plants (Allium sativum L.) over time, **estimating** parameters at three different quantiles and classifying each garlic accession according to its growth rate and asymptotic weight. The nonlinear **regression** model fitted was a Logistic model, and 30 garlic accessions were evaluated. These 30 accessions were divided based **on** the model with the closest quantile estimates; 12 accessions were classified as of lesser interest for planting, 6 were classified as intermediate, and 12 were classified as of greater interest for planting.

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Regarding the results obtained for the rest **interval** duration between successive movements of elbow flexion, it was noted that it approached the theoretical values at speeds of 30, 75 and 120°/s, a tendency, as expected, to be slightly higher, while in the speeds of 240 and 360º/s the rest **interval** were more distant than the theoretical values. It was previously showed that the set speed in isokinetic dynamometers does not re- main constant throughout the range of motion, tending to present a larger variation at higher speeds [9]. Regardless of the rest time range in each experimental condition it may be noticed that all protocols were sufficient to generate fatigue of elbow flexor muscle group. These results agree with the findings of other authors [2,3], which demonstrated that even using a protocol with fewer repetitions there is also a decrease in the muscle work.

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Because An. (Ker.) cruzii was found in great number and that 58% of the specimens were captured in the evening twi- light (Fig. 7), we were able to address its activity pattern dur- ing the 9-hour period of Shannon collections. Accordingly, frequencies of specimens obtained in each 30-minute col- lection intervals were analyzed separate. Based **on** the time of sunset, An. cruzii is an eocrepuscular species (Fig. 8), peak- ing immediately after dusk. During the night (Fig. 9) the ave- rage number of mosquitoes was approximately 200 specimens collected every 30 minutes, decreasing during the twilight (Fig. 10). The frequency of An. cruzii was higher at the sun- set (evening twilight-**interval** p = 0.183, r = 0.625) than at sunrise (morning twilight **interval** p = 0.019, r = 0.882). The mean precipitation and mean temperature, for the

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The performance variables were selected to represent 3 groups of concerns. The first concern was with the empowerment hypothesis tested indirectly using fiscal revenues. The second concern was with public efficiency. To test for public efficiency expenditure data was selected and tested controlling for the service outcome. The third concern was with distribution. Distributive proxies were tested along with proxies for poverty. As expected most of the performance variables are connected with initial income of the municipality. The relationship with size is more complex. Some performance variables are not related with size. The more usual correlation favors smaller municipalities. Since OP municipalities are biased toward richer and larger cities, a simple comparison between averages could overestimate the difference because it did not control for income or underestimate it for not controlling for population. In most of the specifications the performance variables were not correlated with PT votes as mentioned before. The political variable that did influence the performance more often was political competition measured as the difference between the logarithm of votes for the mayor candidate ranked first and second in the first round. Using a parsimonious version of the specification derived in this analysis 6 different methods were applied to estimate the impact of OP implementation **on** the variables of interest change in each mayor tenure. Four of the methods did control for the likelihood of OP implementation and 3 of the methods explored the panel characteristic of the data.

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Smith-Magenis syndrome (SMS) is a complex disorder whose clinical features include mild to severe intellectual disability with speech delay, growth failure, brachycephaly, flat midface, short broad hands, and behavioral problems. SMS is typically caused by a large deletion **on** 17p11.2 that encompasses multiple genes including the retinoic acid-induced 1, RAI1, gene or a mutation in the RAI1 gene. Here we have evaluated 30 patients with suspected SMS and identified SMS- associated classical 17p11.2 deletions in six patients, an atypical deletion of approximately 139 kb that partially deletes the RAI1 gene in one patient, and RAI1 gene nonsynonymous alterations in two patients. Clinical features of patients with or without 17p11.2 deletions and mutations involving the RAI1 gene were compared to identify phenotypes that may be useful in diagnosing patients with SMS.

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Abstract. This study evaluated the milk production performances of the Romanian dairy breed of buffaloes from three important areas of Sălaj county from north-western Romania. This county has one of the valuable buffalo herds from Romania. Comparative analysis in the direction of the main characteristics of buffalo milk production were conducted **on** a herd of 65 buffaloes exploited in extensive traditional conditions. In terms of milk production total lactation it ranks highest value in lactation VIII i.e. 1679.92 kg in area I, 1675.79 kg in lactation VI in area II, respectivelly 1442.53 kg in lactation VI in area III. Differences are less than 1% from normal lactation, with small variations from one lactation to another. These small differences between buffaloes and lactations emphasizes the use of buffaloes for other destinations (traction). The calving **interval** in buffaloes in this study presents the highest values i.e. 485.26 days in lactation VI, area I.

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Electrical inhomogeneity of myocardium may lead poten- tially life threatening cardiac arrhythmias. There are some invasive and non-invasive methods to determine the myocardial inhomogenity. QTd and QTcd are well known non- invasive parameters and can be measured **on** surface ECG by manually or **on** digital platform. Unfortunately repro- ducibility of QT measurements is low both in manual and automatic measurements. 2,3 Additionally, in manuel mea-

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Mapping iQTLs based **on** ultrahigh-density genetic map In recent years, the fast development of high-throughput next- generation sequencing (NGS) technologies has made it practical to obtain a huge number of single nucleotide polymorphism (SNP) markers for population genotyping by DNA sequencing directly [36]. This enables construction of ultrahigh-density genetic maps. For example, two ultrahigh-density genetic maps have been constructed based **on** RI populations in rice [37,38]. In such maps, markers can well represent every position of the genome. Thus, QTL mapping can be performed by testing every marker directly without the need of scanning marker intervals. The model of Eq. (1) can be used for the marker test. But here, the values of the dummy variables x, z and t are determined. Therefore, least squares method can be used to fit the model, and similarly formula (2) can be used to test the imprinting effect of the marker (the putative iQTL). Again, the LOD significance threshold can also be estimated via permutation tests [34]. For distinction, we call this method as point mapping (PM). In addition, analogous to the extension from IM to CIM, PM can also be extended to composite

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Six ferrous artefacts (one bar, three nails, one hinge and one plate) from An- dreatta’s collection (Zequini 2006) were selected for characterization. Three of the iron artefacts (identiied as I82, I84 and I99) were collected at the Ipanema site and the remaining three (identiied as S106, S107 and S111) at the Sardinha site. Metallographic samples were pre- pared and characterized by conventional microscopy, scanning electron micros- copy (SEM) and energy dispersive x-ray spectroscopy microanalysis (Saphire Si(Li) – SUTW EDAX EDX detector) **on** at least 30 inclusions for each ferrous artefact, using 15 kV, area mode and acquisition time of at least 60 s. Micro- analyses **on** the non-metallic inclusions with length and width larger than 5 μm were performed in area mode, covering areas larger than 25 um 2 , imposing as a

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hypotheses that a sub-critical crack growth takes place in all surface micro-cracks. Fishercripps & Collins [11] proposed a modified crack growth model, which is able to predict failure probabilities for both short and long term stresses [11]. Fernandes & Rosa [12, 13] presented a review **on** the “ring-**on**-ring” and “piston-**on**-3-ball” equibiaxial tests for ceramics and glasses, stress distributions in the test pieces were analysed, the importance of the effect of friction at the contact zones was discussed. Based **on** the Weibull statistics and experimental data obtained from testing silica glass rod specimens with diameters between 0.5 and 1 mm [14], a theoretical model was developed for **estimating** their fracture strength under different loading conditions [15]. By this method, the test results of strength from one testing type can be extrapolated to other test types, such as the uniaxial tension, 3-point bending, 4-point bending, etc. Besides, Rosa et al [16] studied the subcritical crack growth in three engineering ceramics under biaxial conditions, the results from the ring-**on**- ring tests were compared with 4-point bending tests.

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The common identification strategy in the literature for this kind of study is a comparative interrupted time series (CITS) design frequently associated with an instru- mental variable. In this case, we propose a mixed strategy involving CITS and propensity score matching design where the comparative group is created by the propensity score estimation (SHADISH; COOK; CAMPBELL, 2002). The idea is to create a control group which was just similar do the treatment group (cities with Haitian migration) before the migration waves, based **on** a set of covariates. We used as covariates a set of variables related to the socioeconomic and demographic context of the cities and also labor market outcomes and migration statistics. They are described in the next section.

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