EconPapers    
Economics at your fingertips  
 

Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view

Kairat Mynbaev () and Carlos Martins-Filho

MPRA Paper from University Library of Munich, Germany

Abstract: We define a new bandwidth-dependent kernel density estimator that improves existing convergence rates for the bias, and preserves that of the variation, when the error is measured in L1. No additional assumptions are imposed to the extant literature.

Keywords: Kernel density estimation; higher order kernels; bias reduction (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2016, Revised 2016
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/75902/1/MPRA_paper_75902.pdf original version (application/pdf)

Related works:
Journal Article: Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view (2017) Downloads
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:pra:mprapa:75902

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().

 
Page updated 2020-11-09
Handle: RePEc:pra:mprapa:75902