Robust estimators and their relations
J. Jurečková (1983)
Acta Universitatis Carolinae. Mathematica et Physica
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Displaying similar documents to “Consistency of linear and quadratic least squares estimators in regression models with covariance stationary errors”
Robust estimators and their relations
Redescending M-estimators in regression analysis, cluster analysis and image analysis
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...
On estimation of a covariance function of stationary errors in a nonlinear regression model.
Štulajter, F. (1994)
Acta Mathematica Universitatis Comenianae. New Series
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Exponential regression
Lubomír Kubáček, Ludmila Kubáčková, Eva Tesaříková (2001)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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On the robustness of multiple regression coefficient estimators obtained by the p-point method
C. Platt, Z. Paprzycki (1988)
Applicationes Mathematicae
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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...
Bayes unbiased estimators of parameters of linear trend with autoregressive errors
František Štulajter (1987)
Aplikace matematiky
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The method of least wquares is usually used in a linear regression model for estimating unknown parameters . The case when is an autoregressive process of the first order and the matrix corresponds to a linear trend is studied and the Bayes approach is used for estimating the parameters . Unbiased Bayes estimators are derived for the case of a small number of observations. These estimators are compared with the locally best unbiased ones and with the usual least squares estimators. ...