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 journalArticlepeer-review

10 Citations (Scopus)


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 languageEnglish
Pages (from-to)476–485
Number of pages10
JournalJournal of the Korean Statistical Society
Issue number3
Early online date18 Mar 2017
Publication statusPublished - 1 Sept 2017
Externally publishedYes

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