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http://dx.doi.org/10.5351/KJAS.2017.30.1.057

Bayesian analysis of finite mixture model with cluster-specific random effects  

Lee, Hyejin (Department of Statistics, Duksung Women's University)
Kyung, Minjung (Department of Statistics, Duksung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.30, no.1, 2017 , pp. 57-68 More about this Journal
Abstract
Clustering algorithms attempt to find a partition of a finite set of objects in to a potentially predetermined number of nonempty subsets. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet prior distribution calculates posterior probabilities when the number of clusters was known. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. Examples are given to show how these models perform on real data.
Keywords
clustering analysis; finite mixture model; cluster-specific random effect; Gibbs sampling;
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Times Cited By KSCI : 1  (Citation Analysis)
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