The Restricted and Unrestricted Two-Parameter Estimators

By M. Revan Özkale, Selahattin Kaçıranlar. 2007

233 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9a91cf4e4f9f9ac6197987a467dc2ce533ab523c

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In Statistics and Probability

Citations in the corpus

A Modified New Two-Parameter Estimator in a Linear Regression Model (2019, methodology): “[8] M. R. Özkale and S. Kaçiranlar, “-e restricted and unrestricted two-parameter estimators,” Communications in Statistics- eory and Methods, vol. 36, no. 15, pp. 2707–2725, 2007.”
A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model (2020, methodology): “(9) The KL estimator of α [15] is given by α̂KL = W(k)M(k)α̂, (10) where M(k) = [Ip − kΛ−1] and MSEM (α̂KL) = σ2W(k)M(k)Λ−1M′(k)W′(k) +[W(k)M(k) − Ip]αα′[W(k)M(k) − Ip]′ (11) The two-parameter (TP) estimator of α (Ozkale and Kaçiranlar [8]) is given by α̂TP = Rα̂, (12) where R = (Λ + kIp) −1(Λ + kdI”
On some beta ridge regression estimators: method, simulation and application (2021, methodology): “However, many biased estimators have been introduced to combat multicollinearity in linear regression, such as Stein estimator [4], principal component estimator [5], ridge regression estimator [6], improved ridge estimators [7] contraction estimator [8], modified ridge regression estimator [9], Liu”
A two-parameter estimator in the negative binomial regression model (2014, methodology): “A lot of shrinkage estimators are proposed to solve the multicollinearity problem in linear model, such as the ordinary ridge estimator (RE) by Hoerl and Kennard [3,4], Liu estimator (LE) by Liu [5] and two-parameter estimator by Özkale and Kaciranlar [6].”
Robust Dawoud–Kibria estimator for handling multicollinearity and outliers in the linear regression model (2021, methodology): “Alternatively, Ozkale and Kaciranlar [8] introduced a two-parameter (TP) estimator of β for solving multicollinearity as follows:”