An R project for numerical solution of the Kiefer-Weiss problem

The Kiefer-Weiss problem consists in finding sequential hypothesis tests minimizing the maximum average sample size among all the tests whose error probabilities of the first and second kind do not exceed some prescribed levels.

This repository contains accompanying R program code for three articles:

Novikov, A., Novikov, A., Farkhshatov, F. 2022. A computational approach to the Kiefer-Weiss problem for sampling from a Bernoulli population, Sequential Analysis 41 (02),
pages 198-219, arXiv.org preprint arXiv:2110.04802 [stat.ME]

Novikov, A. and Farkhshatov, F. 2022. Design and performance evaluation in Kiefer-Weiss problems when sampling from discrete exponential families, Sequential Analysis 41 (04), pages 417 – 434, arXiv.org preprint arXiv:2203.13957 [stat.ME]

and

Novikov, A., Novikov, A., Farkhshatov, F. 2023. Numerical solution of Kiefer-Weiss problems when sampling from continuous exponential families, Sequential Analysis 42 (02), pages 189-209

The accompanying code for the first one, covering sampling from a Bernoulli population is placed in bernoulli directory.

The accompanying code for the second paper, covering sampling from binomial, Poisson and negative binomial (Pascal) distributions is placed in discrete directory.

The accompanying code for the third paper covers sampling from a normal population with a known variance and is placed in continuous directory.