A parametric copula-based framework for hypothesis testing using heterogeneous data

Satish G. Iyengar, Pramod K. Varshney, Thyagaraju Damarla

Research output: Contribution to journalArticlepeer-review

79 Scopus citations


We present a parametric framework for the joint processing of heterogeneous data, specifically for a binary classification problem. Processing such a data set is not straightforward as heterogeneous data may not be commensurate. In addition, the signals may also exhibit statistical dependence due to overlapping fields of view. We propose a copula-based solution to incorporate statistical dependence between disparate sources of information. The important problem of identifying the best copula for binary classification problems is also addressed. Computer simulation results are presented to demonstrate the feasibility of our approach. The method is also tested on real-data provided by the National Institute of Standards and Technology (NIST) for a multibiometric face recognition application. Finally, performance limits are derived to study the influence of statistical dependence on classification performance.

Original languageEnglish (US)
Article number5713266
Pages (from-to)2308-2319
Number of pages12
JournalIEEE Transactions on Signal Processing
Issue number5
StatePublished - May 2011


  • Copula theory
  • Kullback-Leibler divergence
  • hypothesis testing
  • multibiometrics
  • multimodal signals
  • multisensor fusion
  • statistical dependence

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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