Optimal Decision Rules for Simple Hypothesis Testing Under General Criterion Involving Error Probabilities


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DÜLEK B., Ozturk C., Gezici S.

IEEE SIGNAL PROCESSING LETTERS, vol.27, pp.261-265, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27
  • Publication Date: 2020
  • Doi Number: 10.1109/lsp.2020.2966330
  • Journal Name: IEEE SIGNAL PROCESSING LETTERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.261-265
  • Hacettepe University Affiliated: Yes

Abstract

The problem of simple M-ary hypothesis testing under a generic performance criterion that depends on arbitrary functions of error probabilities is considered. Using results from convex analysis, it is proved that an optimal decision rule can be characterized as a randomization among at most two deterministic decision rules, each of the form reminiscent to Bayes rule, if the boundary points corresponding to each rule have zero probability under each hypothesis. Otherwise, a randomization among at most M(M - 1) + 1 deterministic decision rules is sufficient. The form of the deterministic decision rules are explicitly specified. Likelihood ratios are shown to be sufficient statistics. Classical performance measures including Bayesian, minimax, Neyman-Pearson, generalized Neyman-Pearson, restricted Bayesian, and prospect theory based approaches are all covered under the proposed formulation. A numerical example is presented for prospect theory based binary hypothesis testing.