Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterion

A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

“…Also, some authors have developed two-parameter estimators to combat the problem of multicollinearity. e authors include Akdeniz and Kaçiranlar [7];Özkale and Kaçiranlar [8]; Sakallıoglu and Kaçıranlar [9]; Yang and Chang [10]; and very recently Roozbeh [11]; Akdeniz and Roozbeh [12]; and Lukman et al [13,14], among others. e objective of this paper is to propose a new oneparameter ridge-type estimator for the regression parameter when the predictor variables of the model are linear or nearto-linearly related.…”

Ridge stochastic restricted estimators in semiparametric linear measurement error models

“…Recently Roozbeh (2018) and Amini and Roozbeh (2015) have used the GCV criterion for selecting the optimal values of both ridge and bandwith parameters (k and λ) simultaneously, in the presence of multicollinearity for the semiparametric regression models. We can also apply the GCV method to select the optimal bandwidth λ and k simultaneously, which minimizes the following GCV function…”

“…Akdeniz et al (2012) considered a difference based ridge regression estimator and a Liu type estimator of the parameters, Akdeniz and Tabakan (2009) discussed restricted ridge estimation in semiparametric regression models. For recent developments in this field see Roozbeh (2018), Jibo and Asar (2017), Aydin et al (2016) and Yuzbasi and Ejaz (2016). Another fundamental assumption in all statistical analyses is that all the observations are correctly observed.…”

New Restricted Liu Estimator in a Partially Linear Model

“…Hu [20] introduced a ridge estimator by the parametric component β. Liu et al [8] introduced a PCR estimator in partially linear models. For more references, one can refer to Roozbeh [21], Roozbeh et al [22], Akdeniz and Roozbeh [23], Roozbeh et al [24], Roozbeh and Hanzah [25], and Wei and Wang [26].…”