scholarly journals Bayesian inference for finite mixtures of generalized linear models with random effects

Psychometrika ◽  
2000 ◽  
Vol 65 (1) ◽  
pp. 93-119 ◽  
Author(s):  
Peter J. Lenk ◽  
Wayne S. DeSarbo
Keyword(s):  
Bayesian Inference ◽  
Random Effects ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Finite Mixtures

scholarly journals Bayesian Inference on Order-Constrained Parameters in Generalized Linear Models

Biometrics ◽  
2003 ◽  
Vol 59 (2) ◽  
pp. 286-295 ◽  
Author(s):  
David B. Dunson ◽  
Brian Neelon
Keyword(s):  
Bayesian Inference ◽  
Generalized Linear Models ◽  
Linear Models

scholarly journals Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions

Biostatistics ◽  
2010 ◽  
Vol 11 (2) ◽  
pp. 317-336 ◽  
Author(s):  
Sylvia Frühwirth-Schnatter ◽  
Saumyadipta Pyne
Keyword(s):  
Bayesian Inference ◽  
Mixture Models ◽  
Random Effects ◽  
Data Augmentation ◽  
Heterogeneous Data ◽  
High Dimensional ◽  
Finite Mixtures ◽  
Truncated Normal ◽  
T Distribution ◽  
Skew Normal

Abstract Skew-normal and skew-t distributions have proved to be useful for capturing skewness and kurtosis in data directly without transformation. Recently, finite mixtures of such distributions have been considered as a more general tool for handling heterogeneous data involving asymmetric behaviors across subpopulations. We consider such mixture models for both univariate as well as multivariate data. This allows robust modeling of high-dimensional multimodal and asymmetric data generated by popular biotechnological platforms such as flow cytometry. We develop Bayesian inference based on data augmentation and Markov chain Monte Carlo (MCMC) sampling. In addition to the latent allocations, data augmentation is based on a stochastic representation of the skew-normal distribution in terms of a random-effects model with truncated normal random effects. For finite mixtures of skew normals, this leads to a Gibbs sampling scheme that draws from standard densities only. This MCMC scheme is extended to mixtures of skew-t distributions based on representing the skew-t distribution as a scale mixture of skew normals. As an important application of our new method, we demonstrate how it provides a new computational framework for automated analysis of high-dimensional flow cytometric data. Using multivariate skew-normal and skew-t mixture models, we could model non-Gaussian cell populations rigorously and directly without transformation or projection to lower dimensions.

Generalized Linear Models with Random Effects

2006 ◽  
Author(s):  
Youngjo Lee ◽  
John A. Nelder ◽  
Yudi Pawitan
Keyword(s):  
Random Effects ◽  
Generalized Linear Models ◽  
Linear Models

scholarly journals A Family of Generalized Linear Models for Repeated Measures with Normal and Conjugate Random Effects

2010 ◽  
Vol 25 (3) ◽  
pp. 325-347 ◽  
Author(s):  
Geert Molenberghs ◽  
Geert Verbeke ◽  
Clarice G. B. Demétrio ◽  
Afrânio M. C. Vieira
Keyword(s):  
Random Effects ◽  
Generalized Linear Models ◽  
Repeated Measures ◽  
Linear Models

scholarly journals A Unified Bayesian Inference Framework for Generalized Linear Models

2018 ◽  
Vol 25 (3) ◽  
pp. 398-402 ◽  
Author(s):  
Xiangming Meng ◽  
Sheng Wu ◽  
Jiang Zhu
Keyword(s):  
Bayesian Inference ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Bayesian Inference Framework
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