Regression estimation by local polynomial fitting for multivariate data streams

Aboubacar Amiri () and Baba Thiam ()
Additional contact information
Aboubacar Amiri: Université Lille 3, LEM-CNRS (UMR 9221), Domaine universitaire du “pont de bois”
Baba Thiam: Université Lille 3, LEM-CNRS (UMR 9221), Domaine universitaire du “pont de bois”

Statistical Papers, 2018, vol. 59, issue 2, No 18, 813-843

Abstract: 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.

Keywords: Local polynomial; Data streams; Stochastic approximation; Weakly dependent sequences; Kernel methods; 62G05; 62G08; 62G20; 62L12 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-016-0791-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:59:y:2018:i:2:d:10.1007_s00362-016-0791-6

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-016-0791-6

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().