Décomposition des processus aléatoires, une comparaison entre les méthodes factorielles et l'analyse spectrale
Divergence Processes and Weak Convergence of Likelihood Ratio Processes
Divergences of Gauss-Markov random fields with application to statistical inference
Estimates of Hellinger integrals of infinitely divisible distributions
Estimating interactions in binary lattice data with nearest-neighbor property
Estimation and annealing for Gibbsian fields
Estimation and tests of the discrete probability law based on the empirical generating function, (two dimensional case).
Estimation de la densité du recuit simulé
Estimation of a centrality parameter and random sampling time. I. Necessary conditions for optimality
Estimation of reduced Palm distributions by random methods for Cox processes with unknown probability law
Let , i ≥ 1, be i.i.d. observable Cox processes on [a,b] directed by random measures Mi. Assume that the probability law of the Mi is completely unknown. Random techniques are developed (we use data from the processes ,..., to construct a partition of [a,b] whose extremities are random) to estimate L(μ,g) = E(exp(-(N(g) - μ(g))) | N - μ ≥ 0).
Free area estimation in a dynamic germ-grain model with renewal dropping process.
Functional equations in dynamic programming.
Hazard rate model and statistical analysis of a compound point process
A stochastic process cumulating random increments at random moments is studied. We model it as a two-dimensional random point process and study advantages of such an approach. First, a rather general model allowing for the dependence of both components mutually as well as on covariates is formulated, then the case where the increments depend on time is analyzed with the aid of the multiplicative hazard regression model. Special attention is devoted to the problem of prediction of process behaviour....
Hilbert-space methods in experimental design
How to Detect when a Model Fitted to Observational Data Needs Updating
Mean square error for histograms when estimating Radon-Nikodym derivatives.
Minimax results for estimating integrals of analytic processes
The problem of predicting integrals of stochastic processes is considered. Linear estimators have been constructed by means of samples at N discrete times for processes having a fixed Hölderian regularity s > 0 in quadratic mean. It is known that the rate of convergence of the mean squared error is of order N-(2s+1). In the class of analytic processes Hp, p ≥ 1, we show that among all estimators, the linear ones are optimal. Moreover, using optimal coefficient estimators derived through...
Minimax results for estimating integrals of analytic processes
Mixed parametrization for curved exponential models in homogeneous Markov processes with a finite state space.
Modèles statistiques et systèmes standards de processus de vraisemblance