Bayesian analysis of random partition models with Laplace distribution
분야
자연과학 > 통계학
저자
( Minjung Kyung )
발행기관
한국통계학회
간행물정보
CSAM(Communications for Statistical Applications and Methods) 2017년, 제24권 제5호, 457~480쪽(총24쪽)
파일형식
02707086.pdf [무료 PDF 뷰어 다운로드]
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    영문초록
    We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).
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