Evaluation of the predictive performance of the r-k and r-d class estimators

DOI10.1080/03610926.2015.1076482zbMath1368.62201OpenAlexW2530217030MaRDI QIDQ4976274

Issam Dawoud, Selahattin Kaçıranlar

Publication date: 27 July 2017

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2015.1076482

zbMATH Keywords

multicollinearity\(r\)-\(d\) class estimator\(r\)-\(k\) class estimatorbiased estimationprediction mean square error

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

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Cites Work

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• Mean square error matrix comparison of some estimators in linear regressions with multicollinearity
• A Simulation Study of Some Ridge Regression Estimators under Different Distributional Assumptions
• Evaluation of the Predictive Performance of the Liu Estimator
• On the almost unbiased ridge regression estimator
• On Some Ridge Regression Estimators: An Empirical Comparisons
• A note on combining ridge and principal component regression
• A new class of blased estimate in linear regression
• On the almost unbiased generalized liu estimator and unbiased estimation of the bias and mse
• COMBINING THE LIU ESTIMATOR AND THE PRINCIPAL COMPONENT REGRESSION ESTIMATOR
• MEAN SQUARED ERROR COMPARISONS OF SOME BIASED REGRESSION ESTIMATORS
• Choosing Ridge Parameter for Regression Problems
• Performance of Some New Ridge Regression Estimators
• Using Liu-Type Estimator to Combat Collinearity
• More on Liu-Type Estimator in Linear Regression
• Performance of Kibria's Method for the Heteroscedastic Ridge Regression Model: Some Monte Carlo Evidence

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