Bayesian nonparametric multivariate ordinal regression

被引：12

作者：

Bao, Junshu ^{[1
]}

Hanson, Timothy E. ^{[1
]}

机构：

[1] Univ S Carolina, Dept Stat, Columbia, SC 29208 USA

来源：

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2015年 / 43卷 / 03期

关键词：

Finite mixture of probits; linear dependent Dirichlet process; multivariate linear model; DIRICHLET; MODELS; BIVARIATE;

D O I：

10.1002/cjs.11253

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Multivariate ordinal data are modelled as a finite stick-breaking mixture of multivariate probit models. Parametric multivariate probit models are first developed for ordinal data, then generalized to finite mixtures of multivariate probit models. Specific recommendations for prior settings are found to work well in simulations and data analyses. Interpretation of the model is carried out by examining aspects of the mixture components as well as through averaged effects focusing on the mean responses. A simulation verifies that the fitting technique works, and an analysis of alcohol drinking behaviour data illustrates the usefulness of the proposed model. (C) 2015 Statistical Society of Canada

引用

页码：337 / 357

页数：21