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A Bayesian nonparametric goodness of fit test for right censored data based on approximate samples from the beta‐Stacy process
Author(s) -
Al Labadi Luai,
Zarepour Mahmoud
Publication year - 2013
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11188
Subject(s) - goodness of fit , nonparametric statistics , bayesian probability , generalization , dirichlet process , statistical hypothesis testing , statistics , mathematics , econometrics , computer science , mathematical analysis
In recent years, Bayesian nonparametric statistics has received extraordinary attention. The beta‐Stacy process, a generalization of the Dirichlet process, is a fundamental tool in studying Bayesian nonparametric statistics. In this article, we derive a simple, yet efficient, way to simulate the beta‐Stacy process. We compare the efficiency of the new approximation to several other well‐known approximations, and we demonstrate a significant improvement. Using the Kolmogorov distance and samples from the beta‐Stacy process, a Bayesian nonparametric goodness of fit test is proposed. The proposed test is very general in the sense that it can be applied to censored and non‐censored observations. Some illustrative examples are included. 41: 466–487; 2013 © 2013 Statistical Society of Canada