Logo
Munich Personal RePEc Archive

A class of nonparametric density derivative estimators based on global Lipschitz conditions

Mynbaev, Kairat and Martins-Filho, Carlos and Aipenova, Aziza (2015): A class of nonparametric density derivative estimators based on global Lipschitz conditions. Published in: Advances in Econometrics , Vol. 36, No. Essays in Honor of Aman Ullah (2016): pp. 591-615.

[thumbnail of MPRA_paper_75909.pdf]
Preview
PDF
MPRA_paper_75909.pdf

Download (350kB) | Preview

Abstract

Estimators for derivatives associated with a density function can be useful in identifying its modes and inflection points. In addition, these estimators play an important role in plug-in methods associated with bandwidth selection in nonparametric kernel density estimation. In this paper we extend the nonparametric class of density estimators proposed by Mynbaev and Martins Filho (2010) to the estimation of $m$-order density derivatives. Contrary to some existing derivative estimators, the estimators in our proposed class have a full asymptotic characterization, including uniform consistency and asymptotic normality. An expression for the bandwidth that minimizes an asymptotic approximation for the estimators' integrated squared error is provided. A Monte Carlo study sheds light on the finite sample performance of our estimators and contrasts it with that of density derivative estimators based on the classical Rosenblatt-Parzen approach.

Atom RSS 1.0 RSS 2.0

Contact us: mpra@ub.uni-muenchen.de

This repository has been built using EPrints software.

MPRA is a RePEc service hosted by Logo of the University Library LMU Munich.