Inference for multiple linear regression model with extended skew normal errors

A. A. Alhamide, Kamarulzaman Ibrahim, M. T. Alodat

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper presents the estimation of the parameters of the multiple linear regression model when errors are assumed to follow the independent extended skew normal distribution. The estimators of the regression parameters are determined using the maximum likelihood and least squares methods. In addition, the asymptotic distributions of the estimators are studied. The properties of the estimators under both approaches are compared based on a simulation study and a real data set is applied for illustration.

Original languageEnglish
Pages (from-to)81-96
Number of pages16
JournalPakistan Journal of Statistics
Volume32
Issue number2
Publication statusPublished - 1 Mar 2016

Fingerprint

Multiple Linear Regression
Linear Regression Model
Skew
Estimator
Skew-normal Distribution
Model Error
Least Square Method
Asymptotic distribution
Maximum Likelihood
Regression
Simulation Study

Keywords

  • Asymptotic distribution
  • Berry-Esseen theorem
  • Extended skew normal distribution
  • Least squares estimator
  • Maximum likelihood estimates
  • Multiple linear regression model
  • Simulation

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Inference for multiple linear regression model with extended skew normal errors. / Alhamide, A. A.; Ibrahim, Kamarulzaman; Alodat, M. T.

In: Pakistan Journal of Statistics, Vol. 32, No. 2, 01.03.2016, p. 81-96.

Research output: Contribution to journalArticle

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