Results 41 to 50 of about 776,217 (345)

Flexible Bayesian Dirichlet mixtures of generalized linear mixed models for count data

Scientific African, 2021
The need to model count data correctly calls for introducting a flexible yet robust model that can sufficiently handle various types of count data. Models such as Ordinary Least Squares (OLS) used in the past were considered unsuitable.
Olumide S. Adesina, Dawud A. Agunbiade, Pelumi E. Oguntunde   +2 more
doaj   +1 more source

A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence [PDF]

, 2015
A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context.
Aas K, Aristidis K Nikoloulopoulos, Brenner H, Chu H, Chu H, Demidenko E, Genest C, Genest C, Genest C, Hamza TH, Hoyer A, Hua L, Hult H, Joe H, Joe H, Joe H, Joe H, Joe H, Karageorgopoulos DE, Kurowicka D, Kuss O, Leeflang MMG, Leeflang MMG, Ma X, Mavridis D, McNeil AJ, Nash JC, Nelsen RB, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Panagiotelis A, Paul M, Serfling RJ, Shen C, Stroud AH, Vuong QH, Ye Y   +41 more
core   +2 more sources

Bayesian multilevel modelling of the association between socio-economic status and stunting among under-five-year children in Tanzania

Journal of Health, Population and Nutrition, 2023
Background Stunting is associated with socioeconomic status (SES) which is multidimensional. This study aimed to compare different SES indices in predicting stunting.
Edwin Musheiguza, Tukae Mbegalo, Justine N. Mbukwa   +2 more
doaj   +1 more source

Bayesian Model Selection for Generalized Linear Mixed Models

Biometrics, 2023
AbstractWe propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics.
Shuangshuang Xu, Marco A. R. Ferreira, Erica M. Porter, Christopher T. Franck   +3 more
openaire   +3 more sources

Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models [PDF]

Journal of Sciences, Islamic Republic of Iran
When discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. However, the maximum likelihood methods are plagued with substantial calculations for large data sets ...
Mohsen Mohammadzadeh, Leyla Salehi
doaj   +1 more source

Statistical model assumptions achieved by linear models: classics and generalized mixed

Revista Ciência Agronômica, 2020
When an agricultural experiment is completed and the data about the response variable is available, it is necessary to perform an analysis of variance.
Rita Carolina de Melo, Nicole Trevisani, Marcio dos Santos, Altamir Frederico Guidolin, Jefferson Luís Meirelles Coimbra   +4 more
doaj   +1 more source

MCMC methods for multi-response generalized linear mixed models

, 2010
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form.
J. Hadfield
semanticscholar   +1 more source

glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling

The R Journal, 2017
Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error ...
Mollie E. Brooks, K. Kristensen, K. J. Benthem, A. Magnusson, Casper W. Berg, Anders Nielsen, H. Skaug, M. Mächler, B. Bolker   +8 more
semanticscholar   +1 more source

General Design Bayesian Generalized Linear Mixed Models

Statistical Science, 2006
Published at http://dx.doi.org/10.1214/088342306000000015 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Zhao, Yihua, Staudenmayer, John, Coull, Brent, Wand, M.   +3 more
openaire   +4 more sources

The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

bioRxiv, 2016
The coefficient of determination R2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest.
S Nakagawa, P. C. Johnson, H. Schielzeth
semanticscholar   +1 more source