@article {IOPORT.02166026,
    author       = {Goria, M.N. and Leonenko, N.N. and Mergel, V.V. and Novi Inverardi, P.L.},
    title        = {A new class of random vector entropy estimators and its applications in testing statistical hypotheses.},
    year         = {2005},
    journal      = {Journal of Nonparametric Statistics},
    volume       = {17},
    number       = {3},
    issn         = {1048-5252},
    pages        = {277-297},
    publisher    = {Taylor \& Francis Ltd., Abingdon, Oxfordshire},
    doi          = {10.1080/104852504200026815},
    abstract     = {Summary: This paper proposes a new class of estimators of an unknown entropy of random vectors. 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.},
    identifier   = {02166026},
}