Publication | Open Access
Entropic representation and estimation of diversity indices
28
Citations
18
References
2016
Year
Mixture DistributionEngineeringMachine LearningData ScienceDiversity TechniquePattern RecognitionEntropyNatural DiversityMultilinear Subspace LearningDiversity IndicesStatistical InferenceDiversityEntropic BasisEntropic RepresentationStatisticsUnsupervised Machine LearningLinear Diversity Indices
This paper serves a twofold purpose. First, a unified perspective on diversity indices is introduced based on an entropic basis. It is shown that the class of all linear combinations of the entropic basis, referred to as the class of linear diversity indices, covers a wide range of diversity indices used in the literature. Second, a class of estimators for linear diversity indices is proposed and it is shown that these estimators have rapidly decaying biases and asymptotic normality.
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