Detection of Modeling Misspecification Using Cross-Entropy Test

Ofir Krauz, Joseph Tabrikian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In many signal processing applications and learning problems, modeling misspecification may dramatically degrade the estimation performance. Thus, it is useful to detect possible misspecification in the model using the observations. In this work, a composite hypothesis test for detection of modeling misspecification is proposed in the non-Bayesian framework. The test is based on the estimated cross-entropy and thus it is called cross-entropy test (CET). It is computed by the negative of the maximized log-likelihood with respect to the unknown deterministic parameters, where the unknown deterministic parameters are substituted with their maximum likelihood estimates. It is shown that under some mild conditions, the proposed test has the constant false-alarm rate property. The CET performance is evaluated and compared via simulations to White's test for modeling misspecification detection, which is based on information matrix equality. In an example of direction-of-arrival estimation using a miscalibrated array of sensors, it is shown that unlike White's test, the CET is able to detect model misspecification.

Original languageEnglish
Title of host publication2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages520-524
Number of pages5
ISBN (Electronic)9781728155494
DOIs
StatePublished - 1 Dec 2019
Event8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Le Gosier, Guadeloupe
Duration: 15 Dec 201918 Dec 2019

Publication series

Name2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings

Conference

Conference8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
Country/TerritoryGuadeloupe
CityLe Gosier
Period15/12/1918/12/19

Keywords

  • Misspecification
  • composite hypotheses testing
  • cross-entropy
  • mismatch
  • misspecified model

ASJC Scopus subject areas

  • Control and Optimization
  • Artificial Intelligence
  • Computer Networks and Communications

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