Algorithm 93. Non-central F or beta distribution
I. Siatkowski (1988)
Applicationes Mathematicae
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I. Siatkowski (1988)
Applicationes Mathematicae
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Ondřej Vencálek (2013)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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In the present paper we investigate performance of the -depth-nearest classifier. This classifier, proposed recently by Vencálek, uses the concept of data depth to improve the classification method known as the -nearest neighbour. Simulation study which is presented here deals with the two-class classification problem in which the considered distributions belong to the family of skewed normal distributions.
Michael Cramer (1996)
RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications
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Otakar Kříž (1998)
Kybernetika
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Marginal problem (see [Kel]) consists in finding a joint distribution whose marginals are equal to the given less-dimensional distributions. Let’s generalize the problem so that there are given not only less-dimensional distributions but also conditional probabilities. It is necessary to distinguish between objective (Kolmogorov) probability and subjective (de Finetti) approach ([Col,Sco]). In the latter, the coherence problem incorporates both probabilities and conditional probabilities...
Martial Guillaud, Jean-Marc Chassery (1991)
Statistique et analyse des données
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Saralees Nadarajah, Samuel Kotz (2004)
Kybernetika
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It is well known that has the beta distribution when and follow the Dirichlet distribution. Linear combinations of the form have also been studied in Provost and Cheong [S. B. Provost and Y.-H. Cheong: On the distribution of linear combinations of the components of a Dirichlet random vector. Canad. J. Statist. 28 (2000)]. In this paper, we derive the exact distribution of the product (involving the Gauss hypergeometric function) and the corresponding moment properties. We also...
Dear, Roger, Drezner, Zvi (1997)
Journal of Applied Mathematics and Decision Sciences
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S. Grzegórski (1971)
Applicationes Mathematicae
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