Nonlinear regression models based on the normal mean-variance mixture of Birnbaum-Saunders distribution

被引：0

作者：

Mehrdad Naderi

Alireza Arabpour

Tsung-I Lin

Ahad Jamalizadeh

机构：

[1] Shahid Bahonar University of Kerman,Department of Statistics, Faculty of Mathematics and Computer

[2] Shahid Bahonar University of Kerman,Young Researchers Society

[3] National Chung Hsing University,Institute of Statistics

[4] China Medical University,Department of Public Health

来源：

Journal of the Korean Statistical Society | 2017年 / 46卷

关键词：

62F10; 62J02; Birnbaum-Saunders distribution; ECME algorithm; GIG distribution; Nonlinear regression;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper presents a new extension of nonlinear regression models constructed by assuming the normal mean-variance mixture of Birnbaum-Saunders distribution for the unobserved error terms. A computationally analytical EM-type algorithm is developed for computing maximum likelihood estimates. The observed information matrix is derived for obtaining the asymptotic standard errors of parameter estimates. The practical utility of the methodology is illustrated through both simulated and real data sets.

引用

页码：476 / 485

页数：9

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