A robust class of homoscedastic nonlinear regression models. Issue 14 (22nd September 2019)

• Record Type: Journal Article
• Authors: Maleki, Mohsen; Barkhordar, Zahra; Khodadadi, Zahra; Wraith, Darren
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 14(2019)
• Page Start: 2765
• Page End: 2781
• Publication Date: 2019-09-22
• Subjects: ECME-algorithm -- nonlinear regression model -- maximum likelihood estimates -- scale mixtures of normal family -- two-piece distributions; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1635598
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: In this paper, we examine a nonlinear regression ( NLR ) model with homoscedastic errors which follows a flexible class of two-piece distributions based on the scale mixtures of normal ( TP-SMN ) family. The objective of using this family is to develop a robust NLR model. The TP-SMN is a rich class of distributions that covers symmetric/asymmetric and lightly/heavy-tailed distributions and is an alternative family to the well-known scale mixtures of skew-normal ( SMSN ) family studied by Branco and Dey [35]. A key feature of this study is using a new suitable hierarchical representation of the family to obtain maximum-likelihood estimates of model parameters via an EM -type algorithm. The performances of the proposed robust model are demonstrated using simulated and some natural real datasets and also compared to other well-known NLR models.