Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions. Issue 17 (22nd November 2019)

• Record Type: Journal Article
• Authors: Doğru, Fatma Zehra; Yu, Keming; Arslan, Olcay
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 17(2019)
• Page Start: 3213
• Page End: 3240
• Publication Date: 2019-11-22
• Subjects: EM algorithm -- joint location, scale and skewness models -- mixture model -- ML estimation -- SLN -- SN; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1658111
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Joint modelling skewness and heterogeneity is challenging in data analysis, particularly in regression analysis which allows a random probability distribution to change flexibly with covariates. This paper, based on a skew Laplace normal (SLN) mixture of location, scale, and skewness, introduces a new regression model which provides a flexible modelling of location, scale and skewness parameters simultaneously. The maximum likelihood (ML) estimators of all parameters of the proposed model via the expectation-maximization (EM) algorithm as well as their asymptotic properties are derived. Numerical analyses via a simulation study and a real data example are used to illustrate the performance of the proposed model.