In this document, we introduce five additional examples that motivate our research on testing homogeneity. In particular, we show how to check the assumptions for two linear mixed models to validate the asymptotic distributions of the score test statistics. Then, we give the detailed derivation for the score test statistic. We present the result about the local power of the score test statistic...

We provide an estimate of the score function for rank regression using compactly supported wavelets. This estimate is then used to find a rank-based asymptotically efficient estimator for the slope parameter of a linear model. We also provide a consistent estimator of the asymptotic variance of the rank estimator. For related mixed models, the asymptotic relative efficiency is also discussed

Data cloning method is a new computational tool for computing maximum likelihood estimates in complex statistical models such as mixed models. The data cloning method is synthesized with integrated nested Laplace approximation to compute maximum likelihood estimates efficiently via a fast implementation in generalized linear mixed models. Asymptotic normality of the hybrid data cloning based di...

This tutorial demonstrates the usage of BayesX for analysing Bayesian semiparametric regression models based on mixed model methodology. As an example we consider data on undernutrition of children in Zambia. The tutorial is designed to be self-contained and describes all features of BayesX in detail, that will be needed throughout the tutorial. Therefore it may also serve as a first introducti...

Mixed linear models can be used to improve the informational value of milk yield forecasts. For this purpose two different functional approaches for modelling lactation curves are as well compared as three linear mixed models with varying random effects of individual animals and lactation numbers. It can be shown that more complex random regression models fit significantly better than fixed reg...

Transformation of the response of a linear model is a popular method in practice when attempting to satisfy the assumptions of the model. Environmental research routinely uses log-transformations due to the nature of the observed data. The choice of the transformation is often made based upon previous experience or on the comparison of models with different transformed responses. Often a transf...

Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of variables. A clustering algorithm should be able, despite this heterogeneity, extract discriminant pieces information from variables in order design groups. In work we introduce a multilayer architecture model-based method called Mixed Deep Gaussian Mixture Model that can viewed as an automatic way m...

although lot streaming scheduling is an active research field, lot streaming flexible flow lines problems have received far less attention than classical flow shops. this paper deals with scheduling jobs in lot streaming flexible flow line problems. the paper mathematically formulates the problem by a mixed integer linear programming model. this model solves small instances to optimality. moreo...