Flexible Bayesian Dirichlet mixtures of generalized linear mixed models for count data
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 +2 more
doaj +1 more source
Generalized fiducial inference for normal linear mixed models [PDF]
While linear mixed modeling methods are foundational concepts introduced in any statistical education, adequate general methods for interval estimation involving models with more than a few variance components are lacking, especially in the unbalanced ...
Cisewski, Jessi, Hannig, Jan
core +4 more sources
A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence [PDF]
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 +41 more
core +2 more sources
Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting [PDF]
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been expanded to account for additive predictors. In the present paper an approach to variable selection is proposed that works for generalized additive mixed
Groll, Andreas, Tutz, Gerhard
core +4 more sources
Background Stunting is associated with socioeconomic status (SES) which is multidimensional. This study aimed to compare different SES indices in predicting stunting.
Edwin Musheiguza +2 more
doaj +1 more source
Bayesian Model Selection for Generalized Linear Mixed Models
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 +3 more
openaire +3 more sources
Statistical model assumptions achieved by linear models: classics and generalized mixed
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 +4 more
doaj +1 more source
General Design Bayesian Generalized Linear Mixed Models
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 +3 more
openaire +4 more sources
Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships
Our article explores an underused mathematical analytical methodology in the social sciences. In addition to describing the method and its advantages, we extend a previously reported application of mixed models in a well-known database about corruption ...
Luiz Paulo Fávero +4 more
doaj +1 more source
Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models [PDF]
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

