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A nonparametric approach to k-sample inference based on entropy-super-

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Listed:
  • Ashis K. Gangopadhyay
  • Robert disario
  • Dipak K. Dey

Abstract

Entropy as a measure of uncertainty is no longer restricted to the domain of communication theory. It is being used in several branches of statistics. In this paper we consider nonparametric methods of estimation of entropy. Using nonparametric methods, we also develop a test of the hypothesis of equality of entropy for multiple groups. A simulation study is performed to compare the power of the proposed test with existing parametric and nonparametric procedures. Finally a bootstrap distribution of the proposed test statistic is considered for two data sets as illustrative examples.

Suggested Citation

  • Ashis K. Gangopadhyay & Robert disario & Dipak K. Dey, 1997. "A nonparametric approach to k-sample inference based on entropy-super-," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 8(3), pages 237-252, September.
    • Handle: RePEc:taf:gnstxx:v:8:y:1997:i:3:p:237-252
    DOI: 10.1080/10485259708832722
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    References listed on IDEAS

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    1. Harry Joe, 1989. "Estimation of entropy and other functionals of a multivariate density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 41(4), pages 683-697, December.
    2. Peter Hall & Sally Morton, 1993. "On the estimation of entropy," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(1), pages 69-88, March.
    3. P. M. Robinson, 1991. "Consistent Nonparametric Entropy-Based Testing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 437-453.
    4. Härdle,Wolfgang, 1992. "Applied Nonparametric Regression," Cambridge Books, Cambridge University Press, number 9780521429504, October.
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