Multilinear Estimation of Skewness and Kurtosis in Linear Models.
Normal covariances
Note sur les comptabilités markoviennes
On some Mixture Distributions
The aim of this paper is to establish some mixture distributions that arise in stochastic processes. Some basic functions associated with the probability mass function of the mixture distributions, such as k-th moments, characteristic function and factorial moments are computed. Further we obtain a three-term recurrence relation for each established mixture distribution.
On the group pulse processes. I. Classification
On the mode-change problem for random measures.
Optimal sequential procedures with Bayes decision rules
In this article, a general problem of sequential statistical inference for general discrete-time stochastic processes is considered. The problem is to minimize an average sample number given that Bayesian risk due to incorrect decision does not exceed some given bound. We characterize the form of optimal sequential stopping rules in this problem. In particular, we have a characterization of the form of optimal sequential decision procedures when the Bayesian risk includes both the loss due to incorrect...
Optimum designs of experiments for uncorrelated observations on fields
Parametric inference for mixed models defined by stochastic differential equations
Non-linear mixed models defined by stochastic differential equations (SDEs) are considered: the parameters of the diffusion process are random variables and vary among the individuals. A maximum likelihood estimation method based on the Stochastic Approximation EM algorithm, is proposed. This estimation method uses the Euler-Maruyama approximation of the diffusion, achieved using latent auxiliary data introduced to complete the diffusion process between each pair of measurement instants. A tuned...
Poisson sampling for spectral estimation in periodically correlated processes
We study estimation problems for periodically correlated, non gaussian processes. We estimate the correlation functions and the spectral densities from continuous-time samples. From a random time sample, we construct three types of estimators for the spectral densities and we prove their consistency.
Problemas de control estocástico con información incompleta que admiten un proceso suficiente.
Se centra el estudio en los problemas de control estocástico con información incompleta de parámetro discreto.Se define para estos problemas un parámetro suficiente para el proceso básico y se demuestra que la clase de controles basados en éste es esencialmente completa.Como caso particular se estudia el modelo lineal normal y se ve la relación que existe entre el proceso suficiente definido para este modelo y el filtro de Kalman.
Quasi-Likelihood Estimation for Ornstein-Uhlenbeck Diffusion Observed at Random Time Points
2000 Mathematics Subject Classification: 60J60, 62M99.In this paper, we study the quasi-likelihood estimator of the drift parameter θ in the Ornstein-Uhlenbeck diffusion process, when the process is observed at random time points, which are assumed to be unobservable. These time points are arrival times of a Poisson process with known rate. The asymptotic properties of the quasi-likelihood estimator (QLE) of θ, as well as those of its approximations are also elucidated. An extensive simulation study...
Quelques exercices d'utilisation des critères de A. A. Markov
Random perturbations of stochastic processes with unbounded variable length memory.
Recursive linear estimation for discrete-time systems in the presence of different multiplicative observation noises.
Régularité d'ordre quelconque pour un modèle statistique filtré
Some investigations in minimax estimation theory [Book]
Some nonlinear statistical problems of a Poisson process
Some results on exponential families of Markov processes
Spiked Dirichlet process priors for Gaussian process models.