Optimal Generalized Biased Estimator in Linear Regression Model

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...

A New Biased Estimator Derived from Principal Component Regression Estimator

Nearly Optimal Minimax Estimator for High Dimensional Sparse Linear Regression

We present estimators for a well studied statistical estimation problem: the estimation for the linear regression model with soft sparsity constraints (`q constraint with 0 < q ≤ 1) in the high-dimensional setting. We first present a family of estimators, called the projected nearest neighbor estimator and show, by using results from Convex Geometry, that such estimator is within a logarithmic ...

Jackknifed Liu-type Estimator in Poisson Regression Model

The Liu estimator has consistently been demonstrated to be an attractive shrinkage method for reducing the effects of multicollinearity. The Poisson regression model is a well-known model in applications when the response variable consists of count data. However, it is known that multicollinearity negatively affects the variance of the maximum likelihood estimator (MLE) of the Poisson regressio...

On nonnegative garrote estimator in a linear regression model

Abstract: Subset selection regression is a frequently used statistical method. It waives some of the predictor variables and the prediction equation is based on the remaining set of variables. Subset selection is simple and it clearly reduces the variance. An other method for reducing the variance is ridge regression. Usually, subset selection is not as accurate as ridge. The problems with ridg...