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Displaying similar documents to “MSE-Improvement of the Least Squares Estimator by Dropping Variables.”

Least squares estimator consistency: a geometric approach

João Tiago Mexia, João Lita da Silva (2006)

Discussiones Mathematicae Probability and Statistics

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Consistency of LSE estimator in linear models is studied assuming that the error vector has radial symmetry. Generalized polar coordinates and algebraic assumptions on the design matrix are considered in the results that are established.

Sensitivity analysis in linear models

Shuangzhe Liu, Tiefeng Ma, Yonghui Liu (2016)

Special Matrices

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In this work, we consider the general linear model or its variants with the ordinary least squares, generalised least squares or restricted least squares estimators of the regression coefficients and variance. We propose a newly unified set of definitions for local sensitivity for both situations, one for the estimators of the regression coefficients, and the other for the estimators of the variance. Based on these definitions, we present the estimators’ sensitivity results.We include...

A new stochastic restricted biased estimator under heteroscedastic or correlated error

Mustafa Ismaeel Alheety (2011)

ESAIM: Probability and Statistics

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In this paper, under the linear regression model with heteroscedastic and/or correlated errors when the stochastic linear restrictions on the parameter vector are assumed to be held, a generalization of the ordinary mixed estimator (GOME), ordinary ridge regression estimator (GORR) and Generalized least squares estimator (GLSE) is proposed. The performance of this new estimator against GOME, GORR, GLS and the stochastic restricted Liu estimator (SRLE) [Yang and Xu, 50 (2007) 639–647]...