Estimating L-functionals for heavy-tailed distributions and application.
Necir, Abdelhakim, Meraghni, Djamel (2010)
Journal of Probability and Statistics
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Necir, Abdelhakim, Meraghni, Djamel (2010)
Journal of Probability and Statistics
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Necir, Abdelhakim, Rassoul, Abdelaziz, Zitikis, Ričardas (2010)
Journal of Probability and Statistics
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Iacus, Stefano Maria, La Torre, Davide (2005)
Journal of Applied Mathematics and Decision Sciences
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Chen, E.Jack, Li, Min (2010)
Advances in Decision Sciences
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Nuyts, Jean (2010)
International Journal of Mathematics and Mathematical Sciences
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Nicolas Bousquet (2012)
Annales de la faculté des sciences de Toulouse Mathématiques
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The problem of estimating the probability is considered when represents a multivariate stochastic input of a monotonic function . First, a heuristic method to bound , originally proposed by de Rocquigny (2009), is formally described, involving a specialized design of numerical experiments. Then a statistical estimation of is considered based on a sequential stochastic exploration of the input space. A maximum likelihood estimator of build from successive dependent Bernoulli...
Abrams, Aaron, Ganzell, Sandy, Landau, Henry, Landau, Zeph, Pommersheim, James, Zaslow, Eric (2010)
Journal of Probability and Statistics
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Sergey Tarima, Dmitri Pavlov (2006)
ESAIM: Probability and Statistics
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In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of estimates...
Cécile Durot, Karelle Thiébot (2006)
ESAIM: Probability and Statistics
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The paper is concerned with the asymptotic distributions of estimators for the length and the centre of the so-called -shorth interval in a nonparametric regression framework. It is shown that the estimator of the length converges at the -rate to a Gaussian law and that the estimator of the centre converges at the -rate to the location of the maximum of a Brownian motion with parabolic drift. Bootstrap procedures are proposed and shown to be consistent....
Qiao, C.G., Wood, G.R., Lai, C.D., Luo, D.W. (2006)
Journal of Applied Mathematics and Decision Sciences
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