### MSE-Improvement of the Least Squares Estimator by Dropping Variables.

Erkki P. Liski, Götz Trenkler (1993)

Metrika

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Erkki P. Liski, Götz Trenkler (1993)

Metrika

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H. Myoken, Y. Uchida (1977)

Metrika

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G.S. Datta ([unknown])

Metrika

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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.

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]...

Mohamed Boutahar, Claude Deniau (1996)

Metrika

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V.K. Srivastava, A. Chaturvedi (1983)

Metrika

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A. Sahai, S.K. Ray (1980)

Metrika

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Tadeusz Bednarski, Brenton R. Clarke, Daniel Schubert (2010)

Discussiones Mathematicae Probability and Statistics

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In this paper we derive an asymptotic normality result for an adaptive trimmed likelihood estimator of regression starting from initial high breakdownpoint robust regression estimates. The approach leads to quickly and easily computed robust and efficient estimates for regression. A highlight of the method is that it tends automatically in one algorithm to expose the outliers and give least squares estimates with the outliers removed. The idea is to begin with a rapidly computed consistent...

Artur Bryk (2012)

Applicationes Mathematicae

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We consider a fixed-design regression model with long-range dependent errors which form a moving average or Gaussian process. We introduce an artificial randomization of grid points at which observations are taken in order to diminish the impact of strong dependence. We estimate the variance of the errors using the Rice estimator. The estimator is shown to exhibit weak (i.e. in probability) consistency. Simulation results confirm this property for moderate and large sample sizes when...

Csiszár, Imre, Shields, Paul C. (1999)

Electronic Research Announcements of the American Mathematical Society [electronic only]

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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, ...

Christine H. Müller (2004)

Discussiones Mathematicae Probability and Statistics

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We give a review on the properties and applications of M-estimators with redescending score function. For regression analysis, some of these redescending M-estimators can attain the maximum breakdown point which is possible in this setup. Moreover, some of them are the solutions of the problem of maximizing the efficiency under bounded influence function when the regression coefficient and the scale parameter are estimated simultaneously. Hence redescending M-estimators satisfy several...

J. Jurečková (1983)

Acta Universitatis Carolinae. Mathematica et Physica

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