A Numerical Approach to Sequential Multi-Hypothesis Testing for Bernoulli Model

TL;DR

This paper develops a numerical method for designing optimal sequential multi-hypothesis tests for Bernoulli models, minimizing expected sample size under error constraints, and compares it with existing methods like the matrix SPRT.

Contribution

It introduces a computer-oriented minimization approach using Lagrangian multipliers for sequential testing, applicable to various settings including Bayesian and Kiefer-Weiss, with implementation in R.

Findings

The method effectively designs tests minimizing the Lagrangian function.

Numerical evaluations demonstrate the optimality of the proposed tests.

Comparison shows advantages over traditional matrix SPRT in certain scenarios.

Abstract

In this paper we deal with the problem of sequential testing of multiple hypotheses. The main goal is minimizing the expected sample size (ESS) under restrictions on the error probabilities. We take, as a criterion of minimization, a weighted sum of the ESS's evaluated at some points of interest in the parameter space aiming at its minimization under restrictions on the error probabilities. We use a variant of the method of Lagrange multipliers which is based on the minimization of an auxiliary objective function (called Lagrangian) combining the objective function with the restrictions, taken with some constants called multipliers. Subsequently, the multipliers are used to make the solution comply with the restrictions. We develop a computer-oriented method of minimization of the Lagrangian function, that provides, depending on the specific choice of the parameter points, optimal tests in different concrete settings, like in Bayesian, Kiefer-Weiss and other settings. To exemplify the proposed methods for the particular case of sampling from a Bernoulli population we develop a set of computer algorithms for designing sequential tests that minimize the Lagrangian function and for the numerical evaluation of test characteristics like the error probabilities and the ESS, and other related. We implement the algorithms in the R programming language. The program code is available in a public GitHub repository. For the Bernoulli model, we made a series of computer evaluations related to the optimality of sequential multi-hypothesis tests, in a particular case of three hypotheses. A numerical comparison with the matrix sequential probability ratio test is carried out. A method of solution of the multi-hypothesis Kiefer-Weiss is proposed, and is applied for a particular case of three hypotheses in the Bernoulli model.