Risk performance of a pre-test ridge regression estimator under the LINEX loss function when each individual regression coefficient is estimated
From MaRDI portal
Publication:3564756
DOI10.1080/00949650802605739zbMath1187.62127OpenAlexW2123614385MaRDI QIDQ3564756
Publication date: 26 May 2010
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949650802605739
Related Items (3)
Finite sample properties of an HPT estimator when each individual regression coefficient is estimated in a misspecified linear regression model ⋮ MSE Performance and Minimax Regret Significance Points for a HPT Estimator when each Individual Regression Coefficient is Estimated ⋮ MSE dominance of the positive-part shrinkage estimator when each individual regression coefficient is estimated
Cites Work
- Improving the estimation precision for a selected parameter in multiple regression analysis: An algebraic approach
- Generalized ridge regression estimators under the LINEX loss function
- Bayesian Estimation and Prediction Using Asymmetric Loss Functions
- On small sample properties of the almost unbiased generalized ridge estimator
- Finite Sample Properties of Ridge Estimators
- Optimal Critical Values for Pre-Testing in Regression
- A generalized ridge regression estimator and its finite sample properties
- Distribution and density functions of the feasible generalized ridge regression estimator
- Ridge regression in the context of a system of seemingly unrelated regression equations
- A note on new feasible generalized ridge regression estimator under the linex loss function
- A note on the moments of stochastic shrinkage parameters in ridge regression
- A note on almost unbiased generalized ridge regression estimator under asymmetric loss
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
This page was built for publication: Risk performance of a pre-test ridge regression estimator under the LINEX loss function when each individual regression coefficient is estimated
