On Asymptotically Optimal Sequential Bayes Interval Estimation Procedures

Asymptotically Pointwise Optimal Procedures in Sequential Analysis

Optimal sequential procedures with Bayes decision rules

In this article, a general problem of sequential statistical inference for general discrete-time stochastic processes is considered. The problem is to minimize an average sample number given that Bayesian risk due to incorrect decision does not exceed some given bound. We characterize the form of optimal sequential stopping rules in this problem. In particular, we have a characterization of the...

Interval Estimation Naı̈ve Bayes

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with assumptions of conditional independence among features given the class, called naı̈ve Bayes, is competitive with state of the art classifiers. On this paper a new naive Bayes classifier called Interval Estimation naı̈ve Bayes is proposed. Interval Estimation naı̈ve Bayes performs on two phases. On the ...

Asymptotically optimal estimation of smooth functionals for interval

A selection of these reports is available in PostScript form at the Faculty's anonymous ftp-Abstract Estimation in the interval censoring model is considered. A class of smooth functionals is introduced, of which the mean is an example. The asymptotic information lower bound for such functionals can be represented as an inner product of two functions. In case 1, i.e. one observation time per un...

Asymptotically optimal, sequential, multiple testing procedures with prior information on the number of signals

Assuming that data are collected sequentially from independent streams, we consider the simultaneous testing of multiple binary hypotheses under two general setups; when the number of signals (correct alternatives) is known in advance, and when we only have a lower and an upper bound for it. In each of these setups, we propose feasible procedures that control, without any distributional assumpt...