Multilevel minimax hypothesis testing

Kush R. Varshney, Lav R. Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In signal detection, Bayesian hypothesis testing and minimax hypothesis testing represent two extremes in the knowledge of the prior probabilities of the hypotheses: full information and no information. We propose an intermediate formulation, also based on the likelihood ratio test, to allow for partial information. We partition the space of prior probabilities into a set of levels using a quantization-theoretic approach with a minimax Bayes risk error criterion. Within each prior probability level, an optimal representative probability value is found, which is used to set the threshold of the likelihood ratio test. The formulation is demonstrated on signals with additive Gaussian noise.

Original languageEnglish (US)
Title of host publication2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Number of pages4
StatePublished - Sep 5 2011
Externally publishedYes
Event2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France
Duration: Jun 28 2011Jun 30 2011

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings


Other2011 IEEE Statistical Signal Processing Workshop, SSP 2011


  • Bayes risk error
  • categorization
  • hypothesis testing
  • quantization
  • signal detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Multilevel minimax hypothesis testing'. Together they form a unique fingerprint.

Cite this