Rupture de modèles pour des processus de Poisson
Schémas de discrétisation anticipatifs et estimation du paramètre de dérive d'une diffusion
Let YT = (Yt)t∈[0,T] be a real ergodic diffusion process which drift depends on an unkown parameter . Our aim is to estimate θ0 from a discrete observation of the process YT, (Ykδ)k=0,n, for a fixed and small δ, as T = nδ goes to infinity. For that purpose, we adapt the Generalized Method of Moments (see Hansen) to the anticipative and approximate discrete-time trapezoidal scheme, and then to Simpson's. Under some general assumptions, the trapezoidal scheme (respectively Simpson's scheme)...
Schémas de discrétisation anticipatifs et estimation du paramètre de dérive d'une diffusion
Second-order approximation of the entropy in nonlinear least-squares estimation
Semiparametric estimation in autoregressive processes with Markov regime. (Estimación semiparamétrica en procesos autorregresivos con régimen de Markov.)
Sensitivity analysis of -estimators of non-linear regression models
An asymptotic formula for the difference of the -estimates of the regression coefficients of the non-linear model for all observations and for observations is presented under conditions covering the twice absolutely continuous -functions. Then the implications for the -estimation of the regression model are discussed.
Simple large sample estimators of scale and location parameters based on blocks of order statistics.
In this paper quite efficient large sample estimation procedures are derived for jointly estimating the parameters of the location-scale family of distributions. These estimators are linear combinations of the means of suitably chosen blocks of order statistics. For specific distributions, such as the extreme-value, normal, and logistic, little is to be gained by using more than three blocks. For these distributions we can obtain joint relative asymptotic efficiencies of 97-98% using the means of...
Stability in stochastic programming -- The case of unknown location parameter
Statistical analysis of the generalized additive semiparametric survival model with random covariates.
Generalizations of the additive hazards model are considered. Estimates of the regression parameters and baseline function are proposed, when covariates are random. The asymptotic properties of estimators are considered.
Statistical inference for stochastic parabolic equations: a spectral approach.
Statistique asymptotique presque-sûre de modèles statistiques convexes
Statistiques de diffusions et temps local
Strong Consistency of the MLE Under Random Censoring.
Strong convergence bound of the Pareto index estimator under right censoring.
Strong convergence for weighted sums of WOD random variables and its application in the EV regression model
The strong convergence for weighted sums of widely orthant dependent (WOD) random variables is investigated. As an application, we further investigate the strong consistency of the least squares estimator in EV regression model for WOD random variables. A simulation study is carried out to confirm the theoretical results.
Strong law of large numbers for additive extremum estimators
Extremum estimators are obtained by maximizing or minimizing a function of the sample and of the parameters relatively to the parameters. When the function to maximize or minimize is the sum of subfunctions each depending on one observation, the extremum estimators are additive. Maximum likelihood estimators are extremum additive whenever the observations are independent. Another instance of additive extremum estimators are the least squares estimators for multiple regressions when the usual assumptions...
Strong uniform consistency rates of some characteristics of the conditional distribution estimator in the functional single-index model
The aim of this paper is to establish a nonparametric estimate of some characteristics of the conditional distribution. Kernel type estimators for the conditional cumulative distribution function and for the successive derivatives of the conditional density of a scalar response variable Y given a Hilbertian random variable X are introduced when the observations are linked with a single-index structure. We establish the pointwise almost complete convergence and the uniform almost complete convergence...
Strongly consistent estimation in dependent errors-in-variables
Sufficient conditions for the asymptotic efficiency of estimates
Sur l'estimateur des moindres carrés généralisé d'un modèle ARMAX. Application à l'identification des modèles ARMA