Varying dispersion diagnostics for inverse Gaussian regression models

被引：17

作者：

Lin, JG ^{[1
]}

Wei, BC

Zhang, NS

机构：

[1] Southeast Univ, Dept Math, Nanjing 210096, Peoples R China

[2] Zhejiang Univ, Dept Math, Hangzhou, Peoples R China

来源：

JOURNAL OF APPLIED STATISTICS | 2004年 / 31卷 / 10期

基金：

中国国家自然科学基金;

关键词：

adjusted score test; dispersion parameter; inverse Gaussian models; orthogonality transformation; simulation study; varying dispersion;

D O I：

10.1080/0266476042000285512

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：

Homogeneity of dispersion parameters is a standard assumption in inverse Gaussian regression analysis. However, this assumption is not necessarily appropriate. This paper is devoted to the test for varying dispersion in general inverse Gaussian linear regression models. Based on the modified pro. le likelihood ( Cox & Reid, 1987), the adjusted score test for varying dispersion is developed and illustrated with Consumer-Product Sales data (Whitmore, 1986) and Gas vapour data (Weisberg, 1985). The effectiveness of orthogonality transformation and the properties of a score statistic and its adjustment are investigated through Monte Carlo simulations.

引用

页码：1157 / 1170

页数：14

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