Estimation in Dirichlet random effects models
By Minjung Kyung, Jeff Gill, George Casella. 2010
52 citations recorded by the source corpus.
We develop a new Gibbs sampler for a linear mixed model with a Dirich- let process random effect term, which is easily extended to a generalized linear mixed model with a probit link function. Our Gibbs sampler exploits the properties of the multinomial and Dirichlet distributions, and is shown to be an improvement, in terms of operator norm and efficiency, over other commonly used MCMC algorithms. We also investigate methods for the estimation of the precision parameter of the Dirichlet process, finding that maximum likelihood may not be desirable, but a posterior mode is a reasonable approach. Examples are given to show how these models perform on real data. Our results complement both the theoretical basis of the Dirichlet process nonparametric prior and the computational work that has been done to date.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5e1551de35fe40d8887c7d9b9954e2c087f9cb17
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In Artificial Intelligence
- Judith Rousseau
- I. Castillo
- Richard Nickl
- Kolyan Ray
- Christian P. Robert
- Vladimir Spokoiny
- Botond Szab'o
- A. van der Vaart
In Statistics and Probability
- Judith Rousseau
- I. Castillo
- Richard Nickl
- Kolyan Ray
- Christian P. Robert
- Vladimir Spokoiny
- Botond Szab'o
- A. van der Vaart
Citations in the corpus
- On the Frequentist Properties of Bayesian Nonparametric Methods (2016, methodology): “For instance in (Kyung and Casella, 2010) the authors model the distribution of the random effects in a random effect model using a Dirichlet process.”