Another Look at the Naive Estimator in a Regression Model.
Erkki P. Liski, Song-Gui Wang (1994)
Metrika
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Displaying similar documents to “Some Applications of Anderson's Inequality to Some Optimality Criteria of the Generalized Least Squares Estimator.”
Another Look at the Naive Estimator in a Regression Model.
Some Properties of the Distribution of an Operational Ridge Estimator.
V.K. Srivastava, A. Chaturvedi (1983)
Metrika
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An Efficient Estimator Using Auxiliary Information.
A. Sahai, S.K. Ray (1980)
Metrika
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The Generalized Ridge Estimator and Improved Adjustments for Regression Parameters
H. Myoken, Y. Uchida (1977)
Metrika
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MSE-Improvement of the Least Squares Estimator by Dropping Variables.
Erkki P. Liski, Götz Trenkler (1993)
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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]...
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.
Least Squares Estimator for Regression Models with some Deterministic Time Varying Parameters.
Mohamed Boutahar, Claude Deniau (1996)
Metrika
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Consistency of the BIC order estimator.
Csiszár, Imre, Shields, Paul C. (1999)
Electronic Research Announcements of the American Mathematical Society [electronic only]
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A Non-Negativity Criterion for a Certain Variance-Estimator.
A. Chaudhuri (1976)
Metrika
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A new stochastic restricted biased estimator under heteroscedastic or correlated error
Mustafa Ismaeel Alheety (2011)
ESAIM: Probability and Statistics
Similarity:
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, ...
On the Variance of the Ratio Estimator.
T.J. Rao (1972)
Metrika
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Adaptive trimmed likelihood estimation in regression
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...
A Note on Necessary Best Estimator of Order Two .
S.G. Prabhu-Ajgaonkar (1984)
Metrika
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Two-point priors and minimax estimation of a bounded parameter under convex loss
Agata Boratyńska (2005)
Applicationes Mathematicae
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The problem of minimax estimation of a parameter θ when θ is restricted to a finite interval [θ₀,θ₀+m] is studied. The case of a convex loss function is considered. Sufficient conditions for existence of a minimax estimator which is a Bayes estimator with respect to a prior concentrated in two points θ₀ and θ₀+m are obtained. An example is presented.