Estimation and experimental design in a linear regression model using prior information
W. Näther, J. Pilz (1980)
Applicationes Mathematicae
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W. Näther, J. Pilz (1980)
Applicationes Mathematicae
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C. Platt, Z. Paprzycki (1988)
Applicationes Mathematicae
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R. Zmyślony (1976)
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.
Solev, V.N., Haghighi, F. (2004)
Journal of Mathematical Sciences (New York)
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Beniamin Goldys (1985)
Banach Center Publications
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Teresa Ledwina, Jan Mielniczuk (2010)
Applicationes Mathematicae
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The problem of estimating an unknown variance function in a random design Gaussian heteroscedastic regression model is considered. Both the regression function and the logarithm of the variance function are modelled by piecewise polynomials. A finite collection of such parametric models based on a family of partitions of support of an explanatory variable is studied. Penalized model selection criteria as well as post-model-selection estimates are introduced based on Maximum Likelihood...
J. Bartoszewicz (1977)
Applicationes Mathematicae
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Krzysztof B. Janiszowski, Paweł Wnuk (2016)
International Journal of Applied Mathematics and Computer Science
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An approach to estimation of a parametric discrete-time model of a process in the case of some a priori knowledge of the investigated process properties is presented. The knowledge of plant properties is introduced in the form of linear bounds, which can be determined for the coefficient vector of the parametric model studied. The approach yields special biased estimation of model coefficients that preserves demanded properties. A formula for estimation of the model coefficients is derived...
P. Mukhopadhyay (1986)
Metrika
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Jelena Bulatović, Alobodanka Janjić (1979)
Publications de l'Institut Mathématique
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Arijit Chaudhuri, Tapabrata Haiti (1996)
Metrika
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Erichsen, Lars, Brockhoff, Per Bruun (2004)
Journal of Applied Mathematics and Decision Sciences
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S. Trybuła (1974)
Applicationes Mathematicae
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Jan Ámos Víšek (1992)
Kybernetika
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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...
Štulajter, F. (1997)
Acta Mathematica Universitatis Comenianae. New Series
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Herbert Fischer, Stefan Schaffler, Hubert Warsitz (1992)
The Yugoslav Journal of Operations Research
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