The Barnes G function and its relations with sums and products of generalized gamma convolution variables.
Nikeghbali, Ashkan, Yor, Marc (2009)
Electronic Communications in Probability [electronic only]
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Displaying similar documents to “Deviation inequalities and moderate deviations for estimators of parameters in an Ornstein-Uhlenbeck process with linear drift.”
The Barnes G function and its relations with sums and products of generalized gamma convolution variables.
Exponential functionals of Brownian motion. II: Some related diffusion processes.
Matsumoto, Hiroyuki, Yor, Marc (2005)
Probability Surveys [electronic only]
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An extreme Markovian-evolutionary (EME) sequence.
José Tiago de Oliveira (1985)
Trabajos de Estadística e Investigación Operativa
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The most general sequence, with Gumbel margins, generated by maxima procedures in an auto-regressive way (one step) is defined constructively and its properties obtained; some remarks for statistical estimation are presented.
A functional hungarian construction for sums of independent random variables
Ion Grama, Michael Nussbaum (2002)
Annales de l'I.H.P. Probabilités et statistiques
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On the Ritt order and type of a certain class of functions defined by -Dirichletian elements.
Berland, Marcel (1999)
International Journal of Mathematics and Mathematical Sciences
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Laplace asymptotics for generalized K.P.P. equation
Jean-Philippe Rouques (1997)
ESAIM: Probability and Statistics
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Quantitative concentration inequalities on sample path space for mean field interaction
François Bolley (2010)
ESAIM: Probability and Statistics
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We consider the approximation of a mean field stochastic process by a large interacting particle system. We derive non-asymptotic large deviation bounds measuring the concentration of the empirical measure of the paths of the particles around the law of the process. The method is based on a coupling argument, strong integrability estimates on the paths in Hölder norm, and a general concentration result for the empirical measure of identically distributed independent paths. ...
Introducing randomness into first-order and second-order deterministic differential equations.
Moxnes, John F., Hausken, Kjell (2010)
Advances in Mathematical Physics
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Model selection for estimating the non zero components of a Gaussian vector
Sylvie Huet (2006)
ESAIM: Probability and Statistics
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We propose a method based on a penalised likelihood criterion, for estimating the number on non-zero components of the mean of a Gaussian vector. Following the work of Birgé and Massart in Gaussian model selection, we choose the penalty function such that the resulting estimator minimises the Kullback risk.