Efficiency of the restricted \(r\)-\(d\) class estimator in linear regression
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Publication:273357
DOI10.1016/j.amc.2014.03.019zbMath1334.62122OpenAlexW1990863051MaRDI QIDQ273357
Publication date: 21 April 2016
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2014.03.019
\(r\)-\(d\) class estimatorbalanced loss functionprincipal component regression estimatorrestricted \(r\)-\(d\) class estimator
Factor analysis and principal components; correspondence analysis (62H25) Linear regression; mixed models (62J05)
Cites Work
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- Preliminary test Liu estimators based on the conflicting W, LR and LM tests in a regression model with multivariate Student-\(t\) error
- All admissible linear estimators of a regression coefficient under a balanced loss function
- Efficiency of a Liu-type estimator in semiparametric regression models
- Mean square error matrix comparison of some estimators in linear regressions with multicollinearity
- Restricted ridge estimation.
- Least squares estimators in measurement error models under the balanced loss function.
- Ridge regression methodology in partial linear models with correlated errors
- On the restrictedr–kclass estimator and the restrictedr–dclass estimator in linear regression
- Good ridge estimators based on prior information
- A new class of blased estimate in linear regression
- Mean squared error comparisons of the modified ridge regression estimator and iiie restricted ridge regression estimator
- COMBINING THE LIU ESTIMATOR AND THE PRINCIPAL COMPONENT REGRESSION ESTIMATOR
- Using Liu-Type Estimator to Combat Collinearity
- More on the restricted ridge regression estimation
- Principal components regression estimator and a test for the restrictions
- Ridge Estimation to the Restricted Linear Model
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
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