Convergence des suites d'estimateurs ensemblistes du maximum de vraisemblance
Convergence in Distribution of Minimum-Distance Estimators.
Convergence in fractional models and applications.
Convergence of conditional expectations
Convergence rate of the distributions of normalized maximum likelihood estimators for irregular parametric families.
Convergencia del vector de probabilidad a posteriori bajo una distribución predictiva.
La convergencia casi segura de una sucesión de variables aleatorias, con respecto a PX,Q (distribución predictiva), se estudia en relación con la convergencia casi segura, con respecto a PX,θ (para todo θ ∈ Θ), donde {PX,θ}θ ∈ Θ es una familia de modelos de probabilidad sobre el espacio muestral χ.Como consecuencia, se estudia la convergencia casi segura del vector de probabilidad a posteriori con respecto a PX,Q.
Correction to the paper “On the two-sided quality control”
The correction sonsists of deriving correct explicit formulas for MLE of parameters of the normal distribution under the hypothesis .
Corrections of estimators in linearized models
Coupling a stochastic approximation version of EM with an MCMC procedure
The stochastic approximation version of EM (SAEM) proposed by Delyon et al. (1999) is a powerful alternative to EM when the E-step is intractable. Convergence of SAEM toward a maximum of the observed likelihood is established when the unobserved data are simulated at each iteration under the conditional distribution. We show that this very restrictive assumption can be weakened. Indeed, the results of Benveniste et al. for stochastic approximation with markovian perturbations are used to establish...
Coupling a stochastic approximation version of EM with an MCMC procedure
The stochastic approximation version of EM (SAEM) proposed by Delyon et al. (1999) is a powerful alternative to EM when the E-step is intractable. Convergence of SAEM toward a maximum of the observed likelihood is established when the unobserved data are simulated at each iteration under the conditional distribution. We show that this very restrictive assumption can be weakened. Indeed, the results of Benveniste et al. for stochastic approximation with Markovian perturbations are used to establish...
Covariance matrix elements estimation: special linear model without and with repeated measurement
Cross additivity - an application
We try to show that Discriminant Analysis can be considered as a branch of Statistical Decision Theory when viewed from a Bayesian approach. First we present the necessary measure theory results, next we briefly outline the foundations of Bayesian Inference before developing Discriminant Analysis as an application of Bayesian Estimation. Our approach renders Discriminant Analysis more flexible since it gives the possibility of classing an element as belonging to a group of populations. This possibility...