Weak -consistency of the least weighted squares under heteroscedasticity
Jan Ámos Víšek (2011)
Acta Universitatis Carolinae. Mathematica et Physica
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Jan Ámos Víšek (2011)
Acta Universitatis Carolinae. Mathematica et Physica
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Jan Kalina (2011)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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Highly robust statistical and econometric methods have been developed not only as a diagnostic tool for standard methods, but they can be also used as self-standing methods for valid inference. Therefore the robust methods need to be equipped by their own diagnostic tools. This paper describes diagnostics for robust estimation of parameters in two econometric models derived from the linear regression. Both methods are special cases of the generalized method of moments estimator based...
João Tiago Mexia, João Lita da Silva (2007)
Discussiones Mathematicae Probability and Statistics
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Let , 1 ≤ i ≤ n, n ≥ 1 be a linear regression model and suppose that the random errors e₁, e₂, ... are independent and α-stable. In this paper, we obtain sufficient conditions for the strong consistency of the least squares estimator β̃ of β under additional assumptions on the non-random sequence x₁, x₂,... of real vectors.
Štulajter, F. (1994)
Acta Mathematica Universitatis Comenianae. New Series
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Sandra Donevska, Eva Fišerová, Karel Hron (2011)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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Orthogonal regression, also known as the total least squares method, regression with errors-in variables or as a calibration problem, analyzes linear relationship between variables. Comparing to the standard regression, both dependent and explanatory variables account for measurement errors. Through this paper we shortly discuss the orthogonal least squares, the least squares and the maximum likelihood methods for estimation of the orthogonal regression line. We also show that all mentioned...
Štulajter, F. (1997)
Acta Mathematica Universitatis Comenianae. New Series
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TomĂĄĹĄ Jurczyk (2011)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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From the practical point of view the regression analysis and its Least Squares method is clearly one of the most used techniques of statistics. Unfortunately, if there is some problem present in the data (for example contamination), classical methods are not longer suitable. A lot of methods have been proposed to overcome these problematic situations. In this contribution we focus on special kind of methods based on trimming. There exist several approaches which use trimming off part...
Mohamed Boutahar, Claude Deniau (1996)
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...