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.