Results 221 to 230 of about 3,518 (256)

MORE ON THE PRE-TEST ESTIMATOR IN RIDGE REGRESSION

Communications in Statistics - Theory and Methods, 2002
ABSTRACT The problem of estimation of the regression coefficients in a multiple regression model is considered under a multicollinearity situation when it is suspected that the regression coefficients may be restricted to a subspace. The objective of this paper is to compare the usual preliminary test estimator and the preliminary test ridge regression
exaly   +3 more sources

Linearized Restricted Ridge Regression Estimator in Linear Regression

Communications in Statistics - Theory and Methods, 2012
This article primarily aims to put forward the linearized restricted ridge regression (LRRR) estimator in linear regression models. Two types of LRRR estimators are investigated under the PRESS criterion and the optimal LRRR estimators and the optimal restricted generalized ridge regression estimator are obtained.
Xu-Qing Liu
exaly   +2 more sources

Poisson regression diagnostics with ridge estimation

Communications in Statistics - Simulation and Computation, 2021
Influential observations influence the Poisson regression model (PRM) inferences. There are the situations in the PRM, where the explanatory variables are correlated and influential observations oc...
Aamna Khan, Muhammad Aman Ullah, Muhammad Amin   +2 more
openaire   +1 more source

On the estimation of Bell regression model using ridge estimator

Communications in Statistics - Simulation and Computation, 2021
The bell regression is used, when the response variable is in the form of counts with over dispersion.
Muhammad Amin, Muhammad Nauman Akram, Abdul Majid   +2 more
openaire   +1 more source

Ridge Estimators in Logistic Regression

Applied Statistics, 1992
Summary: In this paper it is shown how ridge estimators can be used in logistic regression to improve the parameter estimates and to diminish the error made by further predictions. Different ways to choose the unknown ridge parameter are discussed. The main attention focuses on ridge parameters obtained by cross-validation.
le Cessie, S., van Houwelingen, J. C.
openaire   +2 more sources

A Poisson ridge regression estimator

Economic Modelling, 2011
The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML) method.
Månsson, Kristofer, Shukur, Ghazi
openaire   +2 more sources

Beta ridge regression estimators: simulation and application

Communications in Statistics - Simulation and Computation, 2021
The beta regression model is commonly used when analyzing data that come in the form of rates or percentages.
Mohamed Reda Abonazel, Ibrahim M. Taha
openaire   +1 more source

Shrinkage Ridge Estimators in Linear Regression

Communications in Statistics - Simulation and Computation, 2013
The problem of estimation of the regression coefficients in a multiple regression model (MRM) is considered under multicollinearity situation. Further it is suspected that the regression coefficients may be restricted to a subspace. In this approach, we present the estimators of the regression coefficients combining the idea of preliminary test ...
Mohammad Arashi, S. M. M. Tabatabaey, M. Hassanzadeh Bashtian   +2 more
openaire   +1 more source

New Ridge Regression Estimator in Semiparametric Regression Models

Communications in Statistics - Simulation and Computation, 2015
In the context of ridge regression, the estimation of shrinkage parameter plays an important role in analyzing data. Many efforts have been put to develop the computation of risk function in different full-parametric ridge regression approaches using eigenvalues and then bringing an efficient estimator of shrinkage parameter based on them.
Mahdi Roozbeh, Mohammad Arashi
openaire   +1 more source

Estimating Predictive Variances with Kernel Ridge Regression

2006
In many regression tasks, in addition to an accurate estimate of the conditional mean of the target distribution, an indication of the predictive uncertainty is also required. There are two principal sources of this uncertainty: the noise process contaminating the data and the uncertainty in estimating the model parameters based on a limited sample of ...
Cawley, G., Talbot, N., Chapelle, O.
openaire   +2 more sources