Add to ORKGDescription A permutation test is used for inference in generalized linear models (glm). The procedure is useful when parameter estimates in ordinary glm fail to converge or are unreliable due to small sample size
Inference regarding the inclusion or exclusion of random effects in mixed models is challenging beca...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
The generalized linear mixed model (GLMM) generalizes the standard linear model in three ways: accom...
Abstract We introduce a new R package called glmperm for inference in generalized linear models espe...
The relevance of any possibly significant covariate when using a generalized linear model should be ...
The glm-ie toolbox contains functionality for estimation and inference in generalised linear models ...
In the past half-century, statisticians have recognized the improvement in efficiency of many infere...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
{The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and S...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
A connection between the general linear model (GLM) with frequentist statistical testing and machine...
The Generalized Linear Mixed Model (GLMM) is a natural extension and mixture of a Linear Mixed Model...
Abstract. A nonparametric smoothing method for assessing the adequacy of generalized linear mixed mo...
Maximum likelihood estimation of generalized linear mixed models (GLMMs) is difficult due to margina...
Summary of generalised linear mixed model (GLMM) estimates and test statistics.</p
Inference regarding the inclusion or exclusion of random effects in mixed models is challenging beca...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
The generalized linear mixed model (GLMM) generalizes the standard linear model in three ways: accom...
Abstract We introduce a new R package called glmperm for inference in generalized linear models espe...
The relevance of any possibly significant covariate when using a generalized linear model should be ...
The glm-ie toolbox contains functionality for estimation and inference in generalised linear models ...
In the past half-century, statisticians have recognized the improvement in efficiency of many infere...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
{The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and S...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
A connection between the general linear model (GLM) with frequentist statistical testing and machine...
The Generalized Linear Mixed Model (GLMM) is a natural extension and mixture of a Linear Mixed Model...
Abstract. A nonparametric smoothing method for assessing the adequacy of generalized linear mixed mo...
Maximum likelihood estimation of generalized linear mixed models (GLMMs) is difficult due to margina...
Summary of generalised linear mixed model (GLMM) estimates and test statistics.</p
Inference regarding the inclusion or exclusion of random effects in mixed models is challenging beca...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
The generalized linear mixed model (GLMM) generalizes the standard linear model in three ways: accom...