Estimation of the signal-to-noise in the linear regression model
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Publication:1580846
DOI10.1007/BF02925926zbMath0954.62085MaRDI QIDQ1580846
Publication date: 18 February 2001
Published in: Statistical Papers (Search for Journal in Brave)
least squaressimulationsprincipal componentsmulticollinearityshrunken estimatorsridge estimatorsempirical relative efficiencyplug-in strategiessignal-to-noise
Related Items (10)
The modified Liu-ridge-type estimator: a new class of biased estimators to address multicollinearity ⋮ Premature conclusions about the signal‐to‐noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023) ⋮ Improved preliminary test and Stein-rule Liu estimators for the ill-conditioned elliptical linear regression model ⋮ A New Two-Parameter Estimator in Linear Regression ⋮ Some Liu and ridge-type estimators and their properties under the ill-conditioned Gaussian linear regression model ⋮ Improved ridge regression estimators for the logistic regression model ⋮ Performance of Some New Ridge Regression Estimators ⋮ On Liu-type biased estimators in measurement error models ⋮ On some ridge regression estimators: a nonparametric approach ⋮ Another proposal about the new two-parameter estimator for linear regression model with correlated regressors
Cites Work
- Nonnegative minimum biased quadratic estimation in the linear regression models
- A Note on the Generation of Random Normal Deviates
- A test of the mean square error criterion for shrinkage estimators
- Comparisons among regression estimators under the generalized mean square error criterion
- A Simulation Study of Some Ridge Estimators
- A Monte Carlo Evaluation of Some Ridge-Type Estimators
- Ridge Regression in Practice
- A Comparison of Ridge Estimators
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Ridge Regression: Applications to Nonorthogonal Problems
- Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
- On Biased Estimation in Linear Models
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