The EM algorithm is a frequently advocated algorithm for the estimation of variance components. A faster converging algorithm is developed using alternative parameter-izations based on the analysis of variance. The procedure is exemplified using designs with two and three variance components and wi.th multivariate designs using parameter values relevant to animal breeding data. 1. THE MODEL We ...

Introduction: We aimed to meta-analyze the association of interleukin (IL)-6 -174G>C and -634C>G polymorphisms with Diabetic Nephropathy (DN). Methods: Relevant studies were searched up to September 2015. Heterogeneity among the eligible studies was assessed by chi-square’s Q-statistic and I2 statistics. The random effect model was used for meta-analysis when significant heterogeneity was found...

Article history: Received 10 September 2008 Received in revised form 13 November 2008 Accepted 15 November 2008 The purpose of this study was to assess the effects of management, environmental and temporal factors of farms and batches on mortality and feed consumed per market hog. Information of management, environmental and temporal factors from 158 all-in all-out finishing batches completed i...

Abstract: Meta-analysis is the combination of results from various independent studies. In a meta-analysis, combining survival data from different clinical trials, an important issue is the possible heterogeneity between trials. Such inter-trial variation can not only be explained by heterogeneity of treatment effects across trials but also by heterogeneity of their baseline risk. In addition, ...

This paper examines the asymptotic properties of the popular within, GLS estimators and the Hausman test for panel data models with both large numbers of cross-section (N) and time-series (T) observations. The model we consider includes the regressors with deterministic trends in mean as well as time invariant regressors. If a time-varying regressor is correlated with time invariant regressors,...

I investigate, using the R package spaMM, the effect of misspecification of the smoothing parameter, , of the Matern covariance structure on the mean part of hierarchical generalised linear models (HGLMs) with spatially correlated Gaussian Matern random effects. In particular, by restricting to the set {0.5, 1.5, 2.5} I examine via a simulation study the amount of bias introduced on the fixed e...

Sheep production is affected by genetic and non-genetic factors. A knowledge of these factors is essential for efficient management and for the accurate estimation of breeding values. The objective of this study was to establish the non-genetic factors which affect birth weight and weaning weight in Dorper, Mutton Merino and indigenous Sabi sheep breeds. A total of 2,625 birth and weaning weigh...

Methods We conducted a 6-month prospective cohort study of subjects <18 years who were evaluated for new-onset HSP between February 2009 and June 2010. Weight, height, and casual BP were recorded at 0-7, 60 and 180 days and first-morning urine samples were collected at 0-7, 14, 28, 60, and 180 days after diagnosis. Twenty-four-hour ABPM was completed 6 months after diagnosis. BP measurements we...

Mixed models, with both random and fixed effects, are most often estimated on the assumption that the random effects are normally distributed. In this paper we propose several formal tests of the hypothesis that the random effects and/or errors are normally distributed. Most of the proposed methods can be extended to generalized linear models where tests for non-normal distributions are of inte...

Mixed-effect models are widely used for the analysis of correlated data such as longitudinal data and repeated measures. In this article, we study an approach to the nonparametric estimation of mixed-effect models. We consider models with parametric random effects and flexible fixed effects, and employ the penalized least squares method to estimate the models. The issue to be addressed is the s...