On the restricted Neyman-Pearson approach for composite hypothesis-testing in presence of prior distribution uncertainty

Date
2011
Advisor
Instructor
Source Title
IEEE Transactions on Signal Processing
Print ISSN
1053-587X
Electronic ISSN
Publisher
IEEE
Volume
59
Issue
10
Pages
5056 - 5065
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

The restricted Neyman–Pearson (NP) approach is studied for composite hypothesis-testing problems in the presence of uncertainty in the prior probability distribution under the alternative hypothesis. A restricted NP decision rule aims to maximize the average detection probability under the constraints on the worst-case detection and false-alarm probabilities, and adjusts the constraint on the worst-case detection probability according to the amount of uncertainty in the prior probability distribution. In this study, optimal decision rules according to the restricted NP criterion are investigated. Also, an algorithm is provided to calculate the optimal restricted NP decision rule. In addition, it is shown that the average detection probability is a strictly decreasing and concave function of the constraint on the minimum detection probability. Finally, a detection example is presented to investigate the theoretical results, and extensions to more generic scenarios are provided.

Course
Other identifiers
Book Title
Keywords
Composite hypothesis, Hypothesis-testing, Neyman–Pearson (NP), Restricted Bayes, Max-min
Citation
Published Version (Please cite this version)