Multiple linear regression model with stochastic design variables

Islam M. Q., Tiku M. L.

JOURNAL OF APPLIED STATISTICS, cilt.37, sa.6, ss.923-943, 2010 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 6
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1080/02664760902939612
  • Dergi Adı: JOURNAL OF APPLIED STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.923-943
  • Anahtar Kelimeler: correlation coefficient, least squares, linear regression, modified maximum likelihood, multivariate distributions, non-normality, random design, ROBUST ESTIMATION, MAXIMUM-LIKELIHOOD, BINARY REGRESSION, ESTIMATORS, LOCATION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.