Importance accelerated Robbins-Monro recursion with applications to parametric confidence limits

Efficient Robbins-Monro Procedure for Binary Data

The Robbins-Monro procedure does not perform well in the estimation of extreme quantiles, because the procedure is implemented using asymptotic results, which are not suitable for binary data. Here we propose a modification of the Robbins-Monro procedure and derive the optimal procedure for binary data under some reasonable approximations. The improvement obtained by using the optimal procedure...

The continuous dynamic Robbins-Monro procedure

@bullet Estimation following a Robbins-monro Designed Experiment

Higher Order Representations of the Robbins – Monro Process

For quasi–linear regression functions the Robbins–Monro process Xn is decomposed in a sum of a linear form and a quadratic form both defined in the observation errors. Under regularity conditions the remainder term is of order O(n−3/2) with respect to the Lp-Norm. If a cubic form is added, the remainder term can be improved up to an order of O(n−2). As a corollary the expectation of Xn is expan...

Language Identification With Confidence Limits

A statistical classification algorithm and its application to language identification from noisy input are described. The main innovation is to compute confidence limits on the classification, so that the algorithm terminates when enough evidence to make a clear decision has been made, and so avoiding problems with categories that have similar characteristics. A second application, to genre ide...