This paper is concerned with autocorrelation in errors and multicollinearity among the regressors in linear regression model. To reduce these effects of autocorrelation and multicollinearity, we generalize a principal component two-parameter (PCTP) estimator in the linear regression model with...

In this paper, the cross-validation methods namely the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$C_{p}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>C</mi> <mi>p</mi> </msub> </math> </EquationSource> </InlineEquation>, PRESS and GCV are presented under the multiple linear regression model when multicollinearity exists and additional information imposes restrictions among the parameters that should hold in exact terms. The selection...</equationsource></equationsource></inlineequation>

The purpose of this paper is to introduce a new general Liu-type estimator which includes the ordinary least squares (OLS), ordinary ridge regression (ORR), Liu estimators and some estimators with two biasing parameters as special cases. Also, we investigate the superiority of the new Liu-type...

Recently, Liu (1993) estimator draws an important attention to estimate the regression parameters for an ill-conditioned linear regression model when the vector of errors is distributed according to the law belonging to the class of elliptically contoured distributions (ECDs). This paper...