A comparative study of estimation methods for parameters in multiple linear regression model

CANKAYA S., KAYAALP G., Sangun L., TAHTALI Y., AKAR M.

JOURNAL OF APPLIED ANIMAL RESEARCH, cilt.29, sa.1, ss.43-47, 2006 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 1
  • Basım Tarihi: 2006
  • Doi Numarası: 10.1080/09712119.2006.9706568
  • Dergi Adı: JOURNAL OF APPLIED ANIMAL RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.43-47
  • Anahtar Kelimeler: least squares, least median squares, least trimmed squares, robust regression, non-parametric multiple linear regression, Serranus cabrilla, ROBUST REGRESSION
  • Çukurova Üniversitesi Adresli: Evet

Özet

This paper investigated least squares method, non-parametric method and robust regression methods to predict the parameters of multiple regression models. To evaluate these methods, measurements of body weight, total length and fork length of fishes collected from Serranus cabrilla were used. In these regression models, body weight was dependent variable whereas total length and fork length were independent variables. The results show that non-parametric regression method, general additive model, has minimum R-2 value and least median squares has maximum R-2 value, 0.334 and 0.855, respectively.