### Sequential estimation method for the minimum of a quadratic regression function

M. Męczarski (1987)

Applicationes Mathematicae

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M. Męczarski (1987)

Applicationes Mathematicae

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C. Platt, Z. Paprzycki (1988)

Applicationes Mathematicae

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W. Näther, J. Pilz (1980)

Applicationes Mathematicae

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H. Truszczyńska (1987)

Applicationes Mathematicae

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Pavel Boček, Miroslav Šiman (2016)

Kybernetika

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Although many words have been written about two recent directional (regression) quantile concepts, their applications, and the algorithms for computing associated (regression) quantile regions, their software implementation is still not widely available, which, of course, severely hinders the dissemination of both methods. Wanting to partly fill in the gap here, we provide all the codes needed for computing and plotting the multivariate (regression) quantile regions in Octave and MATLAB,...

I. Thomson (1978)

Metrika

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Valéry V. Fedorov, Werner G. Müller (1997)

Metrika

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Tomasz Górecki (2005)

Discussiones Mathematicae Probability and Statistics

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When we apply stacked regression to classification we need only discriminant indices which can be negative. In many situations, we want these indices to be positive, e.g., if we want to use them to count posterior probabilities, when we want to use stacked regression to combining classification. In such situation, we have to use leastsquares regression under the constraint βₖ ≥ 0, k = 1,2,...,K. In their earlier work [5], LeBlanc and Tibshirani used an algorithm given in [4]. However,...

P. Mukhopadhyay (1986)

Metrika

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Erichsen, Lars, Brockhoff, Per Bruun (2004)

Journal of Applied Mathematics and Decision Sciences

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Huang, Xingfang, Qiu, Peihua (2010)

Mathematical Problems in Engineering

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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.

N.D. Shukla (1979)

Metrika

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Arijit Chaudhuri, Tapabrata Haiti (1996)

Metrika

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