Generalized linear regression models incorporating original outcome distributions

被引：1

作者：

de Paula, Marcelo ^{[1
]}

Ribeiro Diniz, Carlos Alberto ^{[2
]}

机构：

[1] UFOB, CCET, Barreiras, BA, Brazil

[2] Univ Fed Sao Carlos, Dept Stat, Rodovia Washington Luis,Km 235, BR-13565905 Sao Carlos, SP, Brazil

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2016年 / 45卷 / 19期

关键词：

Generalized linear models; Maximum-likelihood estimates; s-inflated power series distributions; LOCAL INFLUENCE;

D O I：

10.1080/03610926.2014.948726

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：

In this article, we propose an approach for incorporating continuous and discrete original outcome distributions into the usual exponential family regression models. The new approach is an extension of the works of Suissa (1991) and Suissa and Blais (1995), which present methods to estimate the risk of an event defined in a sample subspace of an original continuous outcome variable. Simulation studies are presented in order to illustrate the performance of the developed methodology. Real data sets are analyzed by using the proposed models.

引用

页码：5762 / 5786

页数：25