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

Mehrdad Naderi; Alireza Arabpour; Tsung-I Lin; Ahad Jamalizadeh

doi:10.1016/j.jkss.2017.02.002

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

Mehrdad Naderi, Alireza Arabpour, Tsung-I Lin^*, Ahad Jamalizadeh^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	476–485
Number of pages	10
Journal	Journal of the Korean Statistical Society
Volume	46
Issue number	3
Early online date	18 Mar 2017
DOIs	https://doi.org/10.1016/j.jkss.2017.02.002
Publication status	Published - 1 Sept 2017
Externally published	Yes

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10.1016/j.jkss.2017.02.002

Cite this

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title = "Nonlinear regression models based on the normal mean-variance mixture of Birnbaum-Saunders distribution",

abstract = "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.",

author = "Mehrdad Naderi and Alireza Arabpour and Tsung-I Lin and Ahad Jamalizadeh",

note = "Funding information: This work was partially supported by the Ministry of Science and Technology of Taiwan under Grant No. MOST 105-2118-M-005-003-MY2.",

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AU - Naderi, Mehrdad

AU - Arabpour, Alireza

AU - Lin, Tsung-I

AU - Jamalizadeh, Ahad

N1 - Funding information: This work was partially supported by the Ministry of Science and Technology of Taiwan under Grant No. MOST 105-2118-M-005-003-MY2.

PY - 2017/9/1

Y1 - 2017/9/1

N2 - 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.

AB - 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.

U2 - 10.1016/j.jkss.2017.02.002

DO - 10.1016/j.jkss.2017.02.002

M3 - Article

SN - 1226-3192

VL - 46

SP - 476

EP - 485

JO - Journal of the Korean Statistical Society

JF - Journal of the Korean Statistical Society

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