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On the Optimality of Sequential Probability Ratio Tests

Theodore K. Matthes
- 01 Mar 1963 - 
- Vol. 34, Iss: 1, pp 18-21
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This article is published in Annals of Mathematical Statistics.The article was published on 1963-03-01 and is currently open access. It has received 19 citations till now. The article focuses on the topics: Sequential probability ratio test & Sequential estimation.

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Journal ArticleDOI

Structure of sequential tests minimizing an expected sample size

TL;DR: In this article, a characterization of the structure of optimal tests for the Koopman-Darmois family is given, and a new proof of the optimality property of these tests is obtained as a corollary.
Journal ArticleDOI

Controlling decision errors with minimal costs: The sequential probability ratio t test.

TL;DR: The most efficient sequential design, the sequential probability ratio test (SPRT), is discussed and it is shown how it is easily implemented for a 2-sample t test using standard statistical software and the robustness of the SPRT against violations of its assumptions is investigated.
Journal ArticleDOI

Operating Characteristic and Average Sample Number of Binary and Multi-Hypothesis Sequential Probability Ratio Test

TL;DR: Based on the governing equations for OC and ASN of the SPRT developed in the previous work, a solution for the general case is proposed that relies on approximating the original test by truncation, that is, truncating the test at some finite time K.
Proceedings Article

Performance analysis of Wald's SPRT with independent but non-stationary log-likelihood ratios

TL;DR: Two inductive equations governing the OC and ASN are developed and they have non-unique solutions in the general case, but do have unique solutions in two special cases: (a) the log-likelihood ratios converge in distributions and (b) the logs have periodic distributions.
Journal ArticleDOI

Performance Analysis of Sequential Probability Ratio Test

TL;DR: In this article, inductive integral equations governing SPRT and Page's cumulative sum test are developed under very general settings, where the bounds can be time-varying and the LLRs are assumed independent but nonstationary.