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A new class of random vector entropy estimators and its applications in testing statistical hypotheses
Authors:
M. N. Goria a;
N. N. Leonenko b;
V. V. Mergel c;
P. L. Novi Inverardi a
| Affiliations: | a Department of Computer and Management Sciences, University of Trento, Trento, Italy |
| b School of Mathematics, Cardiff University, Cardiff, UK | |
| c Department of Statistics, University of Florida, Gainesville, FL, USA |
DOI:
10.1080/104852504200026815
Publication Frequency:
8 issues per year
Subjects:
Mathematical Economics;
Mathematical Finance;
Medical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
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Abstract
This paper proposes a new class of estimators of an unknown entropy of random vector. Its asymptotic unbiasedness and consistency are proved. Further, this class of estimators is used to build both goodness-of-fit and independence tests based on sample entropy. A simulation study indicates that the test involving the proposed entropy estimate has higher power than other well-known competitors under heavy tailed alternatives which are frequently used in many financial applications.
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| Keywords: Entropy; Multivariate density; Estimator; Goodness-of-fit test; Testing independence; Monte Carlo methods |
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