Principal component and related estimation methods in linear regression models
2020
0 görüntülenme
0 i̇ndirme
Danışman: Prof. Dr. Selahattin Kaçıranlar
Özet (EN)
To cope with the multicollinearity problem several biased estimators are proposed as an alternative to the ordinary least squares (OLS) estimator. One of the biased estimators is principal component estimator of Hotelling (1933). In this study, principal component estimator and many kinds of estimator related with principal component estimator are examined. In addition this, two parameter principal component estimator is compared with principal component estimator, class estimator, class estimator and two parameters estimator under the matrix mean square error. A real data set is analyzed to evaluate the performance of mentioned estimators.
Yazar
Dr. Ecem Özkan
Bu Yayına Nasıl Atıf Yapılır
Ecem Özkan (Master Thesis). Principal component and related estimation methods in linear regression models, 2020, Çukurova University.
Anahtar Kelimeler
Lisans
Tüm Hakları Saklıdır
Bu eser belirtilen lisans koşulları altında paylaşılmaktadır.
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