Approximating distribution functions by iterated function systems.
Approximation of the Steady State System State Distribution of the M/G/1 Retrial Queue With Impatient Customers
Approximations et majorations optimales des statistiques d'ordre d'un échantillon
Aspects of analysis of multivariate failure time data.
Multivariate failure time data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated failure time when an individual is followed for the occurrence of two or more types of events for which the individual is simultaneously at risk, or when distinct individuals have depending event times; or more complicated multistate processes where individuals may move among a number of discrete states over...
Asymptotic behavior of the empirical process for gaussian data presenting seasonal long-memory
We study the asymptotic behavior of the empirical process when the underlying data are gaussian and exhibit seasonal long-memory. We prove that the limiting process can be quite different from the limit obtained in the case of regular long-memory. However, in both cases, the limiting process is degenerated. We apply our results to von–Mises functionals and -Statistics.
Asymptotic behavior of the Empirical Process for Gaussian data presenting seasonal long-memory
We study the asymptotic behavior of the empirical process when the underlying data are Gaussian and exhibit seasonal long-memory. We prove that the limiting process can be quite different from the limit obtained in the case of regular long-memory. However, in both cases, the limiting process is degenerated. We apply our results to von–Mises functionals and U-Statistics.
Asymptotic behaviour of the probability-weighted moments and penultimate approximation
The P.O.T. (Peaks-Over-Threshold) approach consists of using the Generalized Pareto Distribution (GPD) to approximate the distribution of excesses over a threshold. We use the probability-weighted moments to estimate the parameters of the approximating distribution. We study the asymptotic behaviour of these estimators (in particular their asymptotic bias) and also the functional bias of the GPD as an estimate of the distribution function of the excesses. We adapt penultimate approximation results...
Asymptotic behaviour of the probability-weighted moments and penultimate approximation
The P.O.T. (Peaks-Over-Threshold) approach consists of using the Generalized Pareto Distribution (GPD) to approximate the distribution of excesses over a threshold. We use the probability-weighted moments to estimate the parameters of the approximating distribution. We study the asymptotic behaviour of these estimators (in particular their asymptotic bias) and also the functional bias of the GPD as an estimate of the distribution function of the excesses. We adapt penultimate approximation results...
Asymptotic behaviour of the quadratic measure of deviation of multivariate density estimates
Asymptotic comparison of maximum likelihood and a rank estimate in simple linear regression model
Asymptotic covariances of empirical processes
Asymptotic distribution of Gumbel statistic in a semi-parametric approach.
Asymptotic distribution of rank statistics used for multivariate testing symmetry (Preliminary communication)
Asymptotic distribution of robust k-nearest neighbour estimator for functional nonparametric models
Asymptotic Distributions of Smoothed Histograms.
Asymptotic Efficiencies of Spacings Tests for Goodness of Fit.
Asymptotic efficiency and robustness of -estimators
Asymptotic expansion and a local limit theorem for the signed Wilcoxon statistic
Asymptotic law of the intergrated quadratic errors of kernel density and regression estimators under the conditions of dependence. (Loi asymptotique des erreurs quadratiques intégrées des estimateurs à noyau de la densité et de la régression sous des conditions de dépendance.)
Asymptotic normality and convergence rates of linear rank statistics under alternatives