Generalized ridge regression, least squares with stochastic prior information, and Bayesian estimators
From MaRDI portal
Publication:1149714
DOI10.1016/0096-3003(80)90002-8zbMath0454.62064OpenAlexW2033840654MaRDI QIDQ1149714
Publication date: 1980
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0096-3003(80)90002-8
ordinary least squares estimatorBayesian estimatorsgeneralized ridge regressionAitken generalized least squaresstochastic prior information
Cites Work
- Combining independent normal mean estimation problems with unknown variances
- Minimax estimation of a multivariate normal mean under arbitrary quadratic loss
- A Monte Carlo comparison of traditional and Stein-rule estimators under squared error loss
- Good and optimal ridge estimators
- Ridge Analysis Following a Preliminary Test of the Shrunken Hypothesis
- Ridge regression iterative estimation of the biasing parameter
- Ridge Regression in Practice
- On the Mean Square Error of Parameter Estimates for Some Biased Estimators
- A Family of Minimax Estimators of the Mean of a Multivariate Normal Distribution
- On Least Squares with Insufficient Observations
- On a Theorem Used in Nonlinear Least Squares
- Improved Estimators for Coefficients in Linear Regression
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Ridge Regression: Applications to Nonorthogonal Problems
- Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
- A Comment on Ridge Regression. Biased Estimation for Non-Orthogonal Problems
- Linear Statistical Inference and its Applications
- On Biased Estimation in Linear Models
- Some Comments on C P
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Generalized ridge regression, least squares with stochastic prior information, and Bayesian estimators