Results 71 to 80 of about 741,734 (189)

G-optimal designs for hierarchical linear models: an equivalence theorem and a nature-inspired meta-heuristic algorithm. [PDF]

Soft comput, 2021
Liu X, Yue R, Zhang Z, Wong WK.
europepmc   +1 more source

Bayesian Model Comparison in Genetic Association Analysis: Linear Mixed Modeling and SNP Set Testing [PDF]

, 2015
We consider the problems of hypothesis testing and model comparison under a flexible Bayesian linear regression model whose formulation is closely connected with the linear mixed effect model and the parametric models for SNP set analysis in genetic ...
Wen, Xiaoquan
core  

Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models

Genetics Selection Evolution, 2010
Background The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals.
Fikse Freddy, Felleki Majbritt, Rönnegård Lars, Mulder Herman A, Strandberg Erling   +4 more
doaj   +1 more source

conting: An R Package for Bayesian Analysis of Complete and Incomplete Contingency Tables

Journal of Statistical Software, 2014
The aim of this paper is to demonstrate the R package conting for the Bayesian analysis of complete and incomplete contingency tables using hierarchical log-linear models. This package allows a user to identify interactions between categorical factors (
Antony M. Overstall, Ruth King
doaj   +1 more source

Bayesian measures of explained variance and pooling in multilevel (hierarchical) models [PDF]

Explained variance (R^2) is a familiar summary of the fit of a linear regression and has been generalized in various ways to multilevel (hierarchical) models.
Andrew Gelman, Iain Pardoe
core  

Analysis of event-related potential difference waves can benefit from linear mixed effects modeling: Recommendations for analyses and general model fitting

Developmental Cognitive Neuroscience
Linear mixed effects models (LMEs) have advantages for analyzing mean amplitude event-related potential (ERP) data. Compared to ANOVA and linear regression, LMEs retain more subjects and yield unbiased parameter estimates by accounting for trial-level ...
Megan J. Heise, Serena K. Mon, Lindsay C. Bowman   +2 more
doaj   +1 more source

Reanalyzing fourth grade math student achievement in Chile: applying Hierarchical Linear Models (HLMs).

RELIEVE: Revista Electrónica de Investigación y Evaluación Educativa, 2006
The purpose of this article is to present the application of Hierarchical Linear Models (HLMs) in reanalyzing fourth grade math student achievement by using 1996 SIMCE (System of Assessing the Quality of Education in Chile) data.
Janet Cadiz
doaj  

Modeling hierarchical structures - Hierarchical Linear Modeling using MPlus

, 2006
article form FENS symposium, 10 pages, 3 ...
openaire   +2 more sources

Human capital, social capital and scientific research in Europe: an application of linear hierarchical models [PDF]

The theory of human capital is one way to explain individual decisions to produce scientific research. However, this theory, even if it reckons the importance of time in science, is too short for explaining the existing diversity of scientific output ...
Mathieu Goudard, Michel Lubrano
core  

Data Analysis Using Hierarchical Generalized Linear Models with R

Journal of Statistical Software, 2019
Carmen Armero
doaj   +1 more source