THE DISTRIBUTION OF STOCHASTIC SHRINKAGE PARAMETERS IN RIDGE REGRESSION

Diagnostic Measures in Ridge Regression Model with AR(1) Errors under the Stochastic Linear Restrictions

Outliers and influential observations have important effects on the regression analysis. The goal of this paper is to extend the mean-shift model for detecting outliers in case of ridge regression model in the presence of stochastic linear restrictions when the error terms follow by an autoregressive AR(1) process. Furthermore, extensions of measures for diagnosing influential observations are ...

Generalized Ridge Regression Estimator in Semiparametric Regression Models

In the context of ridge regression, the estimation of ridge (shrinkage) parameter plays an important role in analyzing data. Many efforts have been put to develop skills and methods of computing shrinkage estimators for different full-parametric ridge regression approaches, using eigenvalues. However, the estimation of shrinkage parameter is neglected for semiparametric regression models. The m...

Finding Optimal Value for the Shrinkage Parameter in Ridge Regression via Particle Swarm Optimization

A multiple regression model has got the standard assumptions. If the data can not satisfy these assumptions some problems which have some serious undesired effects on the parameter estimates arise. One of the problems is called multicollinearity which means that there is a nearly perfect linear relationship between explanatory variables used in a multiple regression model. This undesirable prob...

Shrinkage regression

Modified ridge regression parameters: A comparative Monte Carlo study

In multiple regression analysis, the independent variables should be uncorrelated within each other. If they are highly intercorrelated, this serious problem is called multicollinearity. There are several methods to get rid of this problem and one of the most famous one is the ridge regression. In this paper, we will propose some modified ridge parameters. We will compare our estimators with so...