Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1080/03610918.2019.1702210 | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION |
Keywords | DocType | Volume |
Binomial proportion, Blocked Gibbs sampling, Dirichlet process mixture, Nonparametric prior | Journal | 51 |
Issue | ISSN | Citations |
6 | 0361-0918 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung-In Seo | 1 | 2 | 2.75 |
| Yongku Kim | 2 | 1 | 6.03 |