A nonparametric approach to k-sample inference based on entropy
| Autor: | Dipak K. Dey, Ashis K. Gangopadhyay, Robert disario |
|---|---|
| Rok vydání: | 1997 |
| Předmět: |
Statistics and Probability
Statistics::Theory Kernel density estimation Nonparametric statistics Joint entropy Nonparametric regression Binary entropy function Statistics Test statistic Entropy (information theory) Applied mathematics Statistics Probability and Uncertainty Mathematics Parametric statistics |
| Zdroj: | Journal of Nonparametric Statistics. 8:237-252 |
| ISSN: | 1029-0311 1048-5252 |
| DOI: | 10.1080/10485259708832722 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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