Generalized difference-based weighted mixed almost unbiased liu estimator in semiparametric regression models
Yükleniyor...
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In classical linear regression analysis problems, the ordinary leastsquares (OLS) estimation is the popular method to obtain the regression weights, given the essential assumptions are satisfied. However,
often, in real-life studies, the response data and its associated explanatory variables do not meet the required conditions, in particular under
multicollinearity, and hence results can be misleading. To overcome
such problem, this paper introduces a novel generalized differencebased weighted mixed almost unbiased Liu estimator. The performance of this new estimator is evaluated against the classical estimators
using the mean squared error. This is followed by an approach to
select the Liu parameter and in this context, a non-stochastic weight is
also considered. Monte Carlo simulation experiments are executed to
assess the performance of the new estimator and subsequently,we
illustrate its application to a real-life data example
Açıklama
Anahtar Kelimeler
Differencing matrix, Multicollinearit, Generalized difference, Almost unbiased Liu, Semiparametric regression
Kaynak
Communications in Statistics - Theory and Methods
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
Sayı
Künye
Akdeniz, F., Roozbeh, M., Akdeniz, E., & Khan, N. M. (2020). Generalized difference-based weighted mixed almost unbiased liu estimator in semiparametric regression models. Communications in Statistics - Theory and Methods,
