Measure-Transformed Two-Sample Hotelling Test

Yoni Eder, Koby Todros

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

3 Scopus citations

Abstract

In this paper, a new robust extension of the two-sample Hotelling test (HT) is developed. The proposed extension, called measure-transformed HT (MT-HT), operates by applying a transform to the probability measures of some reshaped versions of the two compared data sets. The considered measure transformation is structured by a non-negative data-weighting function, called MT-function. We show that proper selection of the involved MT-functions can lead to significant enhancement of the decision performance in the presence of heavy-tailed data. Simulation study illustrates the advantages of the proposed test compared to the two-sample HT and other robust extensions.

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 Inc.
Pages256-260
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

  • Detection theory
  • homogeneity testing
  • probability measure transform
  • robust statistics

ASJC Scopus subject areas

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

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