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

被引：11

作者：

Naderi, Mehrdad ^{[1
,2
]}

Arabpour, Alireza ^{[1
]}

Lin, Tsung-I ^{[3
,4
]}

Jamalizadeh, Ahad ^{[1
]}

机构：

[1] Shahid Bahonar Univ Kerman, Fac Math & Comp, Dept Stat, Kerman, Iran

[2] Shahid Bahonar Univ Kerman, Young Researchers Soc, Kerman, Iran

[3] Natl Chung Hsing Univ, Inst Stat, Taichung 402, Taiwan

[4] China Med Univ, Dept Publ Hlth, Taichung 404, Taiwan

来源：

JOURNAL OF THE KOREAN STATISTICAL SOCIETY | 2017年 / 46卷 / 03期

关键词：

Birnbaum-Saunders distribution; ECME algorithm; GIG distribution; Nonlinear regression; SKEW SCALE MIXTURES; EM ALGORITHM; MAXIMUM-LIKELIHOOD; FAMILY; ECM;

D O I：

10.1016/j.jkss.2017.02.002

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：

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. (C) 2017 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.

引用

页码：476 / 485

页数：10

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