Add to ORKGWe aim to promote the use of the modi\ufb01ed pro\ufb01le likelihood, obtained using quasi Monte Carlo integration, for estimating the variance parameters of a GLMM with binary response and independent clusters
Abstract. Estimation of generalized linear mixed models (GLMMs) with non-nested random effects struc...
In the past half-century, statisticians have recognized the improvement in efficiency of many infere...
of the diploma thesis Title: Computational Methods for Maximum Likelihood Estimation in Generalized ...
We aim to promote the use of the modi\ufb01ed pro\ufb01le likelihood, obtained using quasi Monte Car...
Generalized linear mixed models (GLMMs) are useful for modelling longitudinal and clustered data, bu...
Generalized linear mixed models (GLMMs) are useful for modelling longitudinal and clustered data, bu...
AbstractIn view of the cumbersome and often intractable numerical integrations required for a full l...
This paper presents a two-step pseudo likelihood estimation for generalized linear mixed models with...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
Generalized linear mixed models (GLMMs) have become extremely popular in recent years. The main comp...
University of Minnesota Ph.D. dissertation. January 2016. Major: Statistics. Advisors: Charles Geyer...
In small samples it is well known that the standard methods for estimating variance components in a ...
The Generalized Linear Mixed Model (GLMM) is a natural extension and mixture of a Linear Mixed Model...
Maximum likelihood estimation of generalized linear mixed models (GLMMs) is difficult due to margina...
Abstract. Estimation of generalized linear mixed models (GLMMs) with non-nested random effects struc...
In the past half-century, statisticians have recognized the improvement in efficiency of many infere...
of the diploma thesis Title: Computational Methods for Maximum Likelihood Estimation in Generalized ...
We aim to promote the use of the modi\ufb01ed pro\ufb01le likelihood, obtained using quasi Monte Car...
Generalized linear mixed models (GLMMs) are useful for modelling longitudinal and clustered data, bu...
Generalized linear mixed models (GLMMs) are useful for modelling longitudinal and clustered data, bu...
AbstractIn view of the cumbersome and often intractable numerical integrations required for a full l...
This paper presents a two-step pseudo likelihood estimation for generalized linear mixed models with...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
Generalized linear mixed models (GLMMs) have become extremely popular in recent years. The main comp...
University of Minnesota Ph.D. dissertation. January 2016. Major: Statistics. Advisors: Charles Geyer...
In small samples it is well known that the standard methods for estimating variance components in a ...
The Generalized Linear Mixed Model (GLMM) is a natural extension and mixture of a Linear Mixed Model...
Maximum likelihood estimation of generalized linear mixed models (GLMMs) is difficult due to margina...
Abstract. Estimation of generalized linear mixed models (GLMMs) with non-nested random effects struc...
In the past half-century, statisticians have recognized the improvement in efficiency of many infere...
of the diploma thesis Title: Computational Methods for Maximum Likelihood Estimation in Generalized ...