Data dependent asymmetric kernels for estimating the density function
type: Item
instance of: scholarly article
title: Data dependent asymmetric kernels for estimating the density function
zbMATH Open document ID: 1475.62139
author: R. N. Rattihalli
author: S. B. Patil
published in: Sankhyā. Series A
publication date: 2021-05-03T00:00:00Z
review text: In this paper, the authors propose a new smooth density estimator which uses data dependent asymmetric kernels generated by an unimodal symmetric kernel and study its asymptotic properties. Simulation studies and data analysis indicate that the proposed estimator performs better compared to more common estimators based on a symmetric kernel. Multivariate generalizations of the method are also considered with specific attention to the bivariate case.
reviewed by: Alessandro Selvitella
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 62G30
Mathematics Subject Classification ID: 62H10
zbMATH DE Number: 7342645
zbMATH Keywords: asymmetric kernels
zbMATH Keywords: boundary bias
zbMATH Keywords: kernel density estimation
zbMATH Keywords: mean integrated square error (MISE)
zbMATH Keywords: multivariate density function
zbMATH Keywords: order statistics
describes a project that uses: pyuvdata
describes a project that uses: KernSmooth
describes a project that uses: SiZer
MaRDI profile type: Publication
full work available at URL: s13171-019-00171-6
OpenAlex ID: W2963698942
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DOI: 10.1007/S13171-019-00171-6
description: scientific article; zbMATH DE number 7342645