文章基本信息

标题：Bayesian skew-probit regression for binary response data
本地全文：下载
作者：Jorge L. Bazán ; José S. Romeo ; Josemar Rodrigues 等
期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2014
卷号：28
期号：4
页码：467-482
DOI：10.1214/13-BJPS218
语种：English
出版社：Brazilian Statistical Association
摘要：Since many authors have emphasized the need of asymmetric link functions to fit binary regression models, we propose in this work two new skew-probit link functions for the binary response variables. These link functions will be named power probit and reciprocal power probit due to the relation between them including the probit link as a special case. Also, the probit regressions are special cases of the models considered in this work. A Bayesian inference approach using MCMC is developed for real data suggesting that the link functions proposed here are more appropriate than other link functions used in the literature. In addition, simulation study show that the use of probit model will lead to biased estimate of the regression coefficient.