期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 51, 期 6, 页码 2809-2821出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2019.1702210
关键词
Binomial proportion; Blocked Gibbs sampling; Dirichlet process mixture; Nonparametric prior
资金
- National Research Foundation of Korea (NRF) - Korea government (Ministry of Science, ICT & Future Planning) [2017R1C1B1006792]
- National Research Foundation of Korea (NRF) - Ministry of Education [2018R1D1A1B07043352]
- National Research Foundation of Korea [2017R1C1B1006792, 2018R1D1A1B07043352] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper proposes a nonparametric Bayesian approach for density estimation on an open unit interval (0,1) using binomial data. The proposed method efficiently infers the smooth density defined on (0,1) through the transformation of a random variable. A blocked Gibbs sampling procedure based on the stick-breaking representation is provided for practical implementation, which avoids the use of Metropolis-Hastings transition probability.
This paper proposes a nonparametric Bayesian approach based on a density estimation with an open unit interval (0,1) using binomial data. We propose a very efficient nonparametric Bayesian approach method to infer smooth density defined on (0,1) through the transformation of a random variable. For practical implementation, we provide the corresponding blocked Gibbs sampling procedure based on the stick-breaking representation. The greatest advantage of this method is that it does not require us to draw from the complete conditional posterior distribution using a Metropolis-Hastings transition probability because the proposed transformation leads to a pair of conjugate priors and likelihoods. The validity of the proposed method is assessed through simulated and real data analysis.
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