Results 231 to 240 of about 3,518 (256)
Some of the next articles are maybe not open access.

Minimax Linear Regression Estimators With Application to Ridge Regression

Technometrics, 1982
This article considers minimax linear estimation of β in the multiple linear-regression model Y = Xβ + ξ. Some results from European publications are referenced and summarized and some new results are given. These minimax estimators of β can also be classified as ridgeregression estimators with nonstochastic ridge parameters.
Lawrence Peele, Thomas P. Ryan
openaire   +1 more source

ROBUST RIDGE REGRESSION BASED ON AN M‐ESTIMATOR

Australian Journal of Statistics, 1991
SummaryConsider the linear regression model y=β01 +Xβ+ in the usual notation. It is argued that the class of ordinary ridge estimators obtained by shrinking the least squares estimator by the matrix (X1X + kI)‐1X'X is sensitive to outliers in the ^variable.
openaire   +1 more source

Inequality constrained ridge regression estimator

Statistics & Probability Letters, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Toker S.   +2 more
openaire   +2 more sources

Ridge estimator in a mixed Poisson regression model

Communications in Statistics - Simulation and Computation, 2022
Ramajeyam Tharshan   +1 more
openaire   +1 more source

Ridge regression. discussion and comparison of seven Ridge estimators

2013
In the paper, the characteristics of seven different techniques of Ridge Regression are evaluated with respect to the same model. A consumption function with yearly data for Greece is therefore analysed and Monte-Carlo method s employed to check the performance of the estimation methods.
openaire   +1 more source

A mixed estimator interpretation of ridge regression

Social Science Research, 1982
Abstract It is shown that a formal isomorphism between the ridge estimator and the homogeneous case of the Theil-Goldberger mixed estimator leads to a general interpretation of ridge regression as ordinary least squares estimation subject to a prior stochastic constraint that all slope coefficients in the model are zero. Users of ridge regression are
openaire   +1 more source

Heteroscedasticity consistent ridge regression estimators in linear regression model

Communications in Statistics - Simulation and Computation, 2023
Irum Sajjad Dar, Sohail Chand
openaire   +1 more source

Quantile-based robust ridge m-estimator for linear regression model in presence of multicollinearity and outliers

Communications in Statistics Part B: Simulation and Computation, 2021
Sohail Chand   +2 more
exaly  

On the estimation of Bell regression model using ridge estimator

Communications in Statistics Part B: Simulation and Computation, 2023
, Muhammad Nauman Akram
exaly  

New quantile based ridge M-estimator for linear regression models with multicollinearity and outliers

Communications in Statistics Part B: Simulation and Computation, 2023
Sohail Chand   +2 more
exaly  

Home - About - Disclaimer - Privacy