In the present study, records on 22872 first-parity Holsteins collected from 131 herds by the Animal Breeding and Improvement Center of Iran from 1995 to 2014 were considered to estimate genetic and phenotypic relationships between calving difficulty (CD) and the lactation curve traits, including initial milk yield (Ap), ascending (Bp) and descending (Cp) slope of the lactation curves, peak mil...

Rank-Based methods for iid linear models have been developed over the past 30 years. However, little work has been done in the area of mixed models. In this paper, we discuss a transformation approach to modeling a particular mixed model: one with an arbitrary number of fixed effects and covariates but only one random effect. Discussion of the asymptotic theory is given and the results of a sim...

Robust rank-based methods are proposed for the analysis of data from multi-center clinical trials using a mixed model (including covariates) in which the treatment effects are assumed to be fixed and the center effects are assumed to be random. These rankbased methods are developed under the usual mixed model structure but without the normality assumption of the random components in the model. ...

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...

Linear mixed models are popular models for use with clustered and longitudinal data due to their ability to model variation at different levels of clustering. A Monte Carlo study was used to explore the impact of assumption violations on the bias of parameter estimates and the empirical type I error rates. Simulated conditions included in this study are: simulated serial correlation structure, ...

Tensor product smooths provide the natural way of representing smooth interaction terms in regression models because they are invariant to the units in which the covariates are measured, hence avoiding the need for arbitrary decisions about relative scaling of variables. They would also be the natural way to represent smooth interactions in mixed regression models, but for the fact that the ten...

Based on the Q-function, the conditional expectation of the logarithm of the jointlikelihood between responses and random effects, we propose a case-deletion approach to identify influential subjects and influential observations in linear mixed models. The models considered here are very broad in the sense that any covariance structures can be specified in the covariance matrices of the random ...

1 Mixture Model and Bayesian Model Interpretations Combining Equations 2.1 and 2.2 (Main Text), we can consider the empirical-Bayes hierarchical model as a mixed model with both fixed and random effects in the frequentist setting as given by: logit(pmi) = αm + ZmγXmi +Xmiτ tmm, where ZmγXmi would be considered a fixed effect and Xmiτ tmm a random effect. Alternatively, this model may be viewed ...

Correlated data are fairly common in health and social sciences. For example, clustered data arise when subjects are nested in clusters such as classrooms, hospitals, and neighborhoods; while longitudinal data result from multiple measures for the same subject over long period of time; whereas repeated measures data are involved in multiple measurements for the same subject over time or other d...

The main objective of this paper is to provide experimental designs for the estimation of fixed effects and two variance components, in the presence of nested random effects. Random nested factors arise from quantity designations such as lot or batch, and from sampling and measurement procedures. We introduce a general class of designs for mixed-effects models with random nested factors, called...