On thresholds for robust goodness-of-fit tests

Jayakrishnan Unnikrishnan, Sean Meyn, Venugopal V. Veeravalli

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


Goodness-of-fit tests are statistical procedures used to test the hypothesis H0 that a set of observations were drawn according to some given probability distribution. Decision thresholds used in goodness-of-fit tests are typically set for guaranteeing a target false-alarm probability. In many popular testing procedures results on the weak convergence of the test statistics are used for setting approximate thresholds when exact computation is infeasible. In this work, we study robust procedures for goodness-of-fit where accurate models are not available for the distribution of the observations under hypothesis H0. We develop procedures for setting thresholds in two specific examples a robust version of the Kolmogorov-Smirnov test for continuous alphabets and a robust version of the Hoeffding test for finite alphabets.

Original languageEnglish (US)
Title of host publication2010 IEEE Information Theory Workshop, ITW 2010 - Proceedings
StatePublished - 2010
Event2010 IEEE Information Theory Workshop, ITW 2010 - Dublin, Ireland
Duration: Aug 30 2010Sep 3 2010

Publication series

Name2010 IEEE Information Theory Workshop, ITW 2010 - Proceedings


Other2010 IEEE Information Theory Workshop, ITW 2010

ASJC Scopus subject areas

  • Information Systems
  • Applied Mathematics


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