Multiple linear regression model with stochastic design variables
JOURNAL OF APPLIED STATISTICS, cilt.37, sa.6, ss.923-943, 2010 (SCI-Expanded, Scopus)
- 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.