Regression estimation by local polynomial fitting for multivariate data streams View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-06

AUTHORS

Aboubacar Amiri, Baba Thiam

ABSTRACT

In this paper we study a local polynomial estimator of the regression function and its derivatives. We propose a sequential technique based on a multivariate counterpart of the stochastic approximation method for successive experiments for the local polynomial estimation problem. We present our results in a more general context by considering the weakly dependent sequence of stream data, for which we provide an asymptotic bias-variance decomposition of the considered estimator. Additionally, we study the asymptotic normality of the estimator and we provide algorithms for the practical use of the method in data streams framework. More... »

PAGES

813-843

Journal

TITLE

Statistical Papers

ISSUE

2

VOLUME

59

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00362-016-0791-6

DOI

http://dx.doi.org/10.1007/s00362-016-0791-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045360594


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145 schema:name Université Lille 3, LEM-CNRS (UMR 9221), Domaine universitaire du “pont de bois”, Rue du barreau, BP 60149, 59653, Villeneuve d’Ascq Cedex, France
146 rdf:type schema:Organization
 




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