Variable Selection in Linear Mixed Effects Models.

Bayesian variable selection in linear mixed effects models

Scalable Bayesian Kernel Models with Variable Selection

Nonlinear kernels are used extensively in regression models in statistics and machine learning since they often improve predictive accuracy. Variable selection is a challenge in the context of kernel based regression models. In linear regression the concept of an effect size for the regression coefficients is very useful for variable selection. In this paper we provide an analog for the effect ...

Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation

 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...

Variable Selection in Linear Mixed Effects Models By

This paper is concerned with the selection and estimation of fixed and random effects in linear mixed effects models. We propose a class of nonconcave penalized profile likelihood methods for selecting and estimating important fixed effects. To overcome the difficulty of unknown covariance matrix of random effects, we propose to use a proxy matrix in the penalized profile likelihood. We establi...

Calculation of Inbreeding Depression Effects on Subclinical Mastitis Using Three Different Models

Pedigree information of 3972 Holstein cows and bulls, from 1961 to 2008, was used to calculate inbreeding coefficients and their effects on production traits and subclinical mastitis of dairy farm cows inAzerbaijan province,Iran. Inbreeding was included in a linear mixed model as a class variable. Data were analyzed using the PROC MIXED of SAS by MIVQUE method. Results showed that average inbre...

PERFORMANCE OF DIFFERENT ANT-BASED ALGORITHMS FOR OPTIMIZATION OF MIXED VARIABLE DOMAIN IN CIVIL ENGINEERING DESIGNS

Ant colony optimization algorithms (ACOs) have been basically introduced to discrete variable problems and applied to different research domains in several engineering fields. Meanwhile, abundant studies have been already involved to adapt different ant models to continuous search spaces. Assessments indicate competitive performance of ACOs on discrete or continuous domains. Therefore, as poten...