Multiple linear regression model with stochastic design variables

Islam M. Q., Tiku M. L.

JOURNAL OF APPLIED STATISTICS, vol.37, no.6, pp.923-943, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 6
  • Publication Date: 2010
  • Doi Number: 10.1080/02664760902939612
  • Journal Name: JOURNAL OF APPLIED STATISTICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.923-943
  • Keywords: correlation coefficient, least squares, linear regression, modified maximum likelihood, multivariate distributions, non-normality, random design, ROBUST ESTIMATION, MAXIMUM-LIKELIHOOD, BINARY REGRESSION, ESTIMATORS, LOCATION
  • Middle East Technical University Affiliated: Yes

Abstract

In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.