Comparisons of the \(r\)-\(k\) class estimator to the ordinary least squares estimator under the Pitman's closeness criterion

DOI10.1007/S00362-006-0029-0zbMath1148.62054OpenAlexW2008545906MaRDI QIDQ946259

Selahattin Kaçıranlar, M. Revan Özkale

Publication date: 22 September 2008

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-006-0029-0

zbMATH Keywords

multicollinearity\(r\)-\(k\) class estimatorPitman closeness criterionprincipal components regression estimatorordinary ridge regression estimator

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Point estimation (62F10)

Related Items (7)

A restricted \(r\)-\(k\) class estimator in the mixed regression model with autocorrelated disturbances ⋮ Some comments on: Comparisons of the \(r\)-\(k\) class estimator to the ordinary least squares estimator under the Pitman's closeness criterion. ⋮ The small sample properties of the restricted principal component regression estimator in linear regression model ⋮ PMC theorems on PCR-ridge class estimators ⋮ On the restrictedr–kclass estimator and the restrictedr–dclass estimator in linear regression ⋮ Pitman closeness of the class of isotonic estimators for ordered scale parameters of two gamma distributions ⋮ More on the unbiased ridge regression estimation

Cites Work

• Mean square error matrix comparison of some estimators in linear regressions with multicollinearity
• Comparison of Linear Estimators Using Pitman's Measure of Closeness
• A note on combining ridge and principal component regression
• THE SMALL SAMPLE PROPERTIES OF THE PRINCIPAL COMPONENTS ESTIMATOR FOR REGRESSION COEFFICIENTS
• The pitman nearness criterion and its determination
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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