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Estimation of quadratic density functionals under *m*-dependencePrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); (English)Manuscript (preprint) (Other academic)
##### Abstract [en]

##### Keyword [en]

quadratic density functional, entropy estimation, divergence estimation, stationary m-dependent sequences, Renyi entropy, incomplete U-statistics
##### National Category

Probability Theory and Statistics
##### Research subject

Mathematical Statistics
##### Identifiers

URN: urn:nbn:se:umu:diva-79956OAI: oai:DiVA.org:umu-79956DiVA, id: diva2:645442
#####

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Available from: 2013-09-04 Created: 2013-09-04 Last updated: 2013-10-14Bibliographically approved
##### In thesis

In this paper, we study estimation of certain integral functionals of one or two densities with samples from stationary *m*-dependent sequences. We consider two types of *U*-statistic estimators for these functionals that are functions of the number of epsilon-close vector observations in the samples. We show that the estimators are consistent and obtain their rates of convergence under weak distributional assumptions. In particular, we propose estimators based on incomplete *U*-statistics which have favorable consistency properties even when *m*-dependence is the only dependence condition that can be imposed on the stationary sequences. The results can be used for divergence and entropy estimation, and thus find many applications in statistics and applied sciences.

1. Nonparametric Statistical Inference for Entropy-type Functionals$(function(){PrimeFaces.cw("OverlayPanel","overlay645595",{id:"formSmash:j_idt705:0:j_idt709",widgetVar:"overlay645595",target:"formSmash:j_idt705:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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CiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1194",{id:"formSmash:lower:j_idt1194",widgetVar:"widget_formSmash_lower_j_idt1194",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1195_j_idt1197",{id:"formSmash:lower:j_idt1195:j_idt1197",widgetVar:"widget_formSmash_lower_j_idt1195_j_idt1197",target:"formSmash:lower:j_idt1195:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});