Communications for Statistical Applications and Methods

  • 제24권5호
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  • Pages.457-480
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  • 2017
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Bayesian analysis of random partition models with Laplace distribution

  • Kyung, Minjung (Department of Statistics, Duksung Women's University)
  • 투고 : 2017.04.25
  • 심사 : 2017.08.21
  • 발행 : 2017.09.30
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초록

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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참고문헌

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