Classical and bayesian approaches to the change-point problem : fixed sample and sequential procedures
Condiciones necesarias para pruebas secuenciales truncadas óptimas. Hipótesis simples.
The paper presents a new methodology to obtain partially sequential truncated tests which are optimum in the sense of minimizing the maximum expected sample number. This methodology is based on a variational approach and uses the Lagrange multipliers technique in order to obtain necessary conditions for a test to be optimum. By means of these conditions the optimum test can be obtained. Finally, the method is applied to the problem of testing the mean of an exponential distribution. As a particular...
Continuous stochastic approximation procedure for evaluating the point at which the regression function stops to be non-positive
Convergence of a mimetic finite difference method for static diffusion equation.
Convergence of values in optimal stopping and convergence of optimal stopping times.
Convergence presque sûre et en moyenne des processus d'approximation stochastique, (1ère Partie) processus de Dvoretzky
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...