Equations in one variable over function fields
Enrico Bombieri, Julia Mueller, Umberto Zannier (2001)
Acta Arithmetica
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Enrico Bombieri, Julia Mueller, Umberto Zannier (2001)
Acta Arithmetica
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F. Constantinescu, J. G. Taylor (1973)
Recherche Coopérative sur Programme n°25
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W. J. Ellison (1970-1971)
Séminaire de théorie des nombres de Bordeaux
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Arne Winterhof (2001)
Acta Arithmetica
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Shabbir, Ghulam, Amur, Khuda Bux (2006)
APPS. Applied Sciences
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M. D. Prešić (1970)
Matematički Vesnik
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Jeffrey L. Stuart (2016)
Czechoslovak Mathematical Journal
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R. Magiera (1974)
Applicationes Mathematicae
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Attila Pethő, Michael E. Pohst (2012)
Acta Arithmetica
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Grzegorz Łubczonok (1981)
Colloquium Mathematicae
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Yu-Ru Liu, Craig V. Spencer, Xiaomei Zhao (2010)
Acta Arithmetica
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Alicja Jokiel-Rokita (2006)
Applicationes Mathematicae
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The problem of estimating the mean of a normal distribution is considered in the special case when the data arrive at random times. Certain classes of Bayes sequential estimation procedures are derived under LINEX and reflected normal loss function and with the observation cost determined by a function of the stopping time and the number of observations up to this time.
H. Grosse (1987)
Recherche Coopérative sur Programme n°25
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Stanisław Trybuła
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1. IntroductionThough the theory of minimax estimation was originated about thirty five years ago (see [7], [8], [9], [23]), there are still many unsolved problems in this area. Several papers have been devoted to statistical games in which the set of a priori distributions of the parameter was suitably restricted ([2], [10], [13]). Recently, special attention was paid to the problem of admissibility ([24], [3], [11], [12]).This paper is devoted to the problem of determining minimax...
Ján Minác, Michel Spira (1990)
Mathematische Zeitschrift
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Paulo Ribenboim (1992)
Manuscripta mathematica
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Ryszard Magiera (2001)
Applicationes Mathematicae
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The problem of estimating unknown parameters of Markov-additive processes from data observed up to a random stopping time is considered. To the problem of estimation, the intermediate approach between the Bayes and the minimax principle is applied in which it is assumed that a vague prior information on the distribution of the unknown parameters is available. The loss in estimating is assumed to consist of the error of estimation (defined by a weighted squared loss function) as well...