A Comparison of Mixed and Ridge Estimators of Linear Models

DOI10.1080/03610910802506630zbMath1157.62046OpenAlexW1987996170MaRDI QIDQ3616275

Selahattin Kaçıranlar, Hüseyin Güler

Publication date: 24 March 2009

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910802506630

zbMATH Keywords

ridge regressionmean square errorautocorrelationmulticollinearitymixed estimationeconomic example

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Monte Carlo methods (65C05) Statistical tables (62Q05)

Related Items (10)

Feasible generalized Stein-rule restricted ridge regression estimators ⋮ More on the restricted ridge regression estimation ⋮ Sparsely restricted penalized estimators ⋮ The feasible generalized restricted ridge regression estimator ⋮ New Bayesian approach to the estimation in simultaneous equations model ⋮ A test statistic to choose between Liu-type and least-squares estimator based on mean square error criteria ⋮ A note on the performance of biased estimators with autocorrelated errors ⋮ On a principal component two-parameter estimator in linear model with autocorrelated errors ⋮ \(r\)-\(k\) class estimator in the linear regression model with correlated errors ⋮ Ridge estimation in linear mixed measurement error models with stochastic linear mixed restrictions

Cites Work

• Linear models. Least squares and alternatives
• On the performance of biased estimators in the linear regression model with correlated or heteroscedastic errors
• A Critique of Some Ridge Regression Methods
• Ridge regression:some simulations
• A Monte Carlo Evaluation of Some Ridge-Type Estimators
• Performance of Some New Ridge Regression Estimators
• Error misspecification and properties of the simple ridge estimator
• Best Linear Unbiased Prediction in the Generalized Linear Regression Model
• On the Use of Incomplete Prior Information in Regression Analysis
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
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