Probabilistic Index Models

• 成果类型: Article
• 署名作者: Thas, Olivier; Neve, Jan De; Clement, Lieven; Ottoy, Jean-Pierre
• 署名单位: Ghent University; University of Wollongong; Ghent University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2011.01020.x
• 发表日期: 2012
• 页码: 623-671
• 关键词: intuitive nonparametric approach; longitudinal data-analysis; regression-models; marginal models; kruskal-wallis; ROC curve; fit; size; covariance; EFFICIENCY

摘要

We present a semiparametric statistical model for the probabilistic index which can be defined as P(Y <= Y*), where Y and Y* are independent random response variables associated with covariate patterns X and X* respectively. A link function defines the relationship between the probabilistic index and a linear predictor. Asymptotic normality of the estimators and consistency of the covariance matrix estimator are established through semiparametric theory. The model is illustrated with several examples, and the estimation theory is validated in a simulation study.