Asymptotic Considerations for Selecting the Best Component of a Multivariate Normal Population.
Asymptotic normality of randomly truncated stochastic algorithms
We study the convergence rate of randomly truncated stochastic algorithms, which consist in the truncation of the standard Robbins–Monro procedure on an increasing sequence of compact sets. Such a truncation is often required in practice to ensure convergence when standard algorithms fail because the expected-value function grows too fast. In this work, we give a self contained proof of a central limit theorem for this algorithm under local assumptions on the expected-value function, which are fairly...
Asymptotic optimality of experimental designs in estimating a product of means.
Asymptotic optimality of sequential designs for estimation.
Bayes optimal stopping of a homogeneous poisson process under linex loss function and variation in the prior
A homogeneous Poisson process (N(t),t ≥ 0) with the intensity function m(t)=θ is observed on the interval [0,T]. The problem consists in estimating θ with balancing the LINEX loss due to an error of estimation and the cost of sampling which depends linearly on T. The optimal T is given when the prior distribution of θ is not uniquely specified.
Bayes Sequential Estimation for an Exponential Family of Processes: A Discrete Time Approach.
Bayes sequential estimation procedures for exponential-type processes
The Bayesian sequential estimation problem for an exponential family of processes is considered. Using a weighted square error loss and observing cost involving a linear function of the process, the Bayes sequential procedures are derived.
Bayesian stopping rule in discrete parameter space with multiple local maxima
The paper presents the stopping rule for random search for Bayesian model-structure estimation by maximising the likelihood function. The inspected maximisation uses random restarts to cope with local maxima in discrete space. The stopping rule, suitable for any maximisation of this type, exploits the probability of finding global maximum implied by the number of local maxima already found. It stops the search when this probability crosses a given threshold. The inspected case represents an important...
Bibliography on stochastic approximation
Boundaries for the average length of strategic tests
Bregman superquantiles. Estimation methods and applications
In thiswork,we extend some parameters built on a probability distribution introduced before to the casewhere the proximity between real numbers is measured by using a Bregman divergence. This leads to the definition of the Bregman superquantile (thatwe can connect with severalworks in economy, see for example [18] or [9]). Axioms of a coherent measure of risk discussed previously (see [31] or [3]) are studied in the case of Bregman superquantile. Furthermore,we deal with asymptotic properties of...
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...
Der diskontierte Einarmige Bandit.