Fixed precision estimation of the parameters of a linear regression model with unknown covariance structure
H. Truszczyńska (1987)
Applicationes Mathematicae
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H. Truszczyńska (1987)
Applicationes Mathematicae
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C. Platt, Z. Paprzycki (1988)
Applicationes Mathematicae
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Tadeusz Bednarski (2016)
Discussiones Mathematicae Probability and Statistics
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Computationally attractive Fisher consistent robust estimation methods based on adaptive explanatory variables trimming are proposed for the logistic regression model. Results of a Monte Carlo experiment and a real data analysis show its good behavior for moderate sample sizes. The method is applicable when some distributional information about explanatory variables is available.
Brenton R. Clarke (2000)
Discussiones Mathematicae Probability and Statistics
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In small to moderate sample sizes it is important to make use of all the data when there are no outliers, for reasons of efficiency. It is equally important to guard against the possibility that there may be single or multiple outliers which can have disastrous effects on normal theory least squares estimation and inference. The purpose of this paper is to describe and illustrate the use of an adaptive regression estimation algorithm which can be used to highlight outliers, either single...
R. Zieliński (1973)
Applicationes Mathematicae
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M. Kohler (1998)
Metrika
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Arijit Chaudhuri, Tapabrata Haiti (1996)
Metrika
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R. Zmyślony (1976)
Applicationes Mathematicae
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Hilmar Drygas (2009)
Discussiones Mathematicae Probability and Statistics
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This paper deals with an application of regression analysis to the regulation of the blood-sugar under diabetes mellitus. Section 2 gives a description of Gram-Schmidt orthogonalization, while Section 3 discusses the difference between Gauss-Markov estimation and Least Squares Estimation. Section 4 is devoted to the statistical analysis of the blood-sugar during the night. The response change of blood-sugar is explained by three variables: time, food and physical activity ("Bewegung")....
P. Mukhopadhyay (1986)
Metrika
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I. Thomson (1978)
Metrika
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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...
Ristić, Miroslav M., Popović, Biljana Č. (2001)
Publications de l'Institut Mathématique. Nouvelle Série
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Ristić, Miroslav M., Popović, Biljana Č. (2001)
Publications de l'Institut Mathématique. Nouvelle Série
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M. Męczarski (1987)
Applicationes Mathematicae
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Erichsen, Lars, Brockhoff, Per Bruun (2004)
Journal of Applied Mathematics and Decision Sciences
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