Truncated Sequential Non-Parametric Hypothesis Testing Based on Random Distortion Testing

Prashant Khanduri, Dominique Pastor, Vinod Sharma, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper, we propose a new algorithm for sequential non-parametric hypothesis testing based on Random Distortion Testing (RDT). The data-based approach is non-parametric in the sense that the underlying signal distributions under each hypothesis are assumed to be unknown. Our previously proposed non-truncated sequential algorithm, SeqRDT, was shown to achieve desired error probabilities under a few assumptions on the signal model. In this paper, we show that the proposed truncated sequential algorithm, T-SeqRDT, requires even fewer assumptions on the signal model, while guaranteeing the error probabilities to be below pre-specified levels and at the same time makes a decision faster compared to its optimal fixed-sample-size counterpart, BlockRDT. We derive bounds on the error probabilities and the average stopping times of the algorithm. Via numerical simulations, we compare the performance of T-SeqRDT with SeqRDT, BlockRDT, sequential probability ratio test, and composite sequential probability ratio tests. We also show the robustness of the proposed approach compared with the standard likelihood ratio based approaches.

Original languageEnglish (US)
Article number8736860
Pages (from-to)4027-4042
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume67
Issue number15
DOIs
StatePublished - Aug 1 2019

Keywords

  • Truncated sequential testing
  • non-parametric testing
  • random distortion testing (RDT)
  • robust hypothesis testing
  • sequential probability ratio test (SPRT)

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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