Minimax prediction of the difference of multinomial random variables
S. Trybuła (1991)
Applicationes Mathematicae
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S. Trybuła (1991)
Applicationes Mathematicae
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S. Trybuła (1991)
Applicationes Mathematicae
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Stanisław Trybuła (2003)
Applicationes Mathematicae
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The problem of minimax mutual prediction is considered for multinomial random variables with the loss function being a linear combination of quadratic losses connected with prediction of particular variables. The basic parameter of the minimax mutual predictor is determined by numerical solution of some equation.
S. Trybuła (1991)
Applicationes Mathematicae
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Saralees Nadarajah (2007)
Applicationes Mathematicae
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Data that are proportions arise most frequently in biomedical research. In this paper, the exact distributions of R = X + Y and W = X/(X+Y) and the corresponding moment properties are derived when X and Y are proportions and arise from the most flexible bivariate beta distribution known to date. The associated estimation procedures are developed. Finally, two medical data sets are used to illustrate possible applications.
Nadarajah, Saralees, Gupta, Arjun K. (2005)
International Journal of Mathematics and Mathematical Sciences
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Alicja Jokiel-Rokita (2002)
Applicationes Mathematicae
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A class of minimax predictors of random variables with multinomial or multivariate hypergeometric distribution is determined in the case when the sample size is assumed to be a random variable with an unknown distribution. It is also proved that the usual predictors, which are minimax when the sample size is fixed, are not minimax, but they remain admissible when the sample size is an ancillary statistic with unknown distribution.
Zhang, Hu-Ming, Taylor, Robert L. (1995)
International Journal of Mathematics and Mathematical Sciences
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Bosetto, Elena (1999)
Beiträge zur Algebra und Geometrie
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Andrzej Grzybowski (1991)
Applicationes Mathematicae
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de Finetti, Bruno (2008)
Journal Électronique d'Histoire des Probabilités et de la Statistique [electronic only]
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I. Kotlarski (1962)
Colloquium Mathematicae
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Nadarajah, Saralees (2005)
Journal of Applied Mathematics
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Saralees Nadarajah (2007)
Applicationes Mathematicae
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The gamma and Rayleigh distributions are two of the most applied distributions in engineering. Motivated by engineering issues, the exact distribution of the quotient X/Y is derived when X and Y are independent gamma and Rayleigh random variables. Tabulations of the associated percentage points and a computer program for generating them are also given.
Nadarajah, Saralees, Kotz, Samuel (2005)
Journal of Applied Mathematics and Decision Sciences
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