Principal Component Preliminary Test Estimator in the Linear Regression Model

A New Biased Estimator Derived from Principal Component Regression Estimator

Preliminary test almost unbiased ridge estimator in a linear regression model with multivariate Student-t errors

In this paper, the preliminary test almost unbiased ridge estimators of the regression coefficients based on the conflicting Wald (W), Likelihood ratio (LR) and Lagrangian multiplier (LM) tests in a multiple regression model with multivariate Student-t errors are introduced when it is suspected that the regression coefficients may be restricted to a subspace. The bias and quadratic risks of the...

Combining the Liu-type Estimator and the Principal Component Regression Estimator

In this study a new two-parameter estimator which includes the ordinary least squares (OLS), the principal components regression (PCR) and the Liu-type estimator is proposed. Conditions for the superiority of this new estimator over the PCR, r-k class estimator and Liu-type estimator are derived. Furthermore the performance of this estimator is compared with the other estimators in different co...

A Stochastic Restricted Principal Components Regression Estimator in the Linear Model

We propose a new estimator to combat the multicollinearity in the linear model when there are stochastic linear restrictions on the regression coefficients. The new estimator is constructed by combining the ordinary mixed estimator (OME) and the principal components regression (PCR) estimator, which is called the stochastic restricted principal components (SRPC) regression estimator. Necessary ...

IMPROVED ESTIMATOR OF THE VARIANCE IN THE LINEAR MODEL

The improved estimator of the variance in the general linear model is presented under an asymmetric linex loss function.