Copula-based link functions in binary regression models

被引：0
作者：
M. Mesfioui
T. Bouezmarni
M. Belalia
机构：
[1] Université du Québec à Trois-Rivières,CIREQ, Centre SÈVE
[2] Université de Sherbrooke,Department of Mathematics and Statistics
[3] University of Windsor,undefined
来源：
Statistical Papers | 2023年 / 64卷
关键词：
Copula; Discrete response; Link function; Logistic regression; Semi-parametric estimation; Bootstrap;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The paper proposes a new class of link functions for generalized binary regression based on copula models. The idea consists of writing the predictive probability of success (PPOS) in terms of marginal distributions and the conditional distribution for the copula. The proposed link functions provide flexible models and include the probit regression. A remarkable relationship with the logistic regression is also established in the case of a single covariate. To model the PPOS, a parametric family for the copula is considered and either a parametric or a nonparametric estimator for the marginal distributions is used. The asymptotic properties of these estimators are established and a simulation study is carried out to evaluate their performance. Finally, the methodology is illustrated by analyzing a data set on burn injury.
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页码：557 / 585
页数：28
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