3-stage and 4-stage tests with deterministic stage sizes and non-iid data

TL;DR

This paper introduces new multistage testing procedures with deterministic stage sizes that control error probabilities under non-iid data, achieving asymptotic optimality and robustness compared to classical methods.

Contribution

It proposes novel 3-stage and 4-stage tests with concrete, non-asymptotic designs that match fixed-sample error control and establish asymptotic optimality under non-iid data.

Findings

Achieve asymptotic optimal expected sample size under prescribed distributions.

More robust than Wald's SPRT in one-sided testing with small error probabilities.

Applicable to various non-iid testing problems like autoregression and Markov chains.

Abstract

Given a fixed-sample-size test that controls the error probabilities under two specific, but arbitrary, distributions, a 3-stage and two 4-stage tests are proposed and analyzed. For each of them, a novel, concrete, non-asymptotic, non-conservative design is specified, which guarantees the same error control as the given fixed-sample-size test. Moreover, first-order asymptotic approximation are established on their expected sample sizes under the two prescribed distributions as the error probabilities go to zero. As a corollary, it is shown that the proposed multistage tests can achieve, in this asymptotic sense, the optimal expected sample size under these two distributions in the class of all sequential tests with the same error control. Furthermore, they are shown to be much more robust than Wald's SPRT when applied to one-sided testing problems and the error probabilities under control are small enough. These general results are applied to testing problems in the iid setup and beyond, such as testing the correlation coefficient of a first-order autoregression, or the transition matrix of a finite-state Markov chain, and are illustrated in various numerical studies.