A nonparametric data based univariate density function estimate
type: Item
instance of: scholarly article
title: A nonparametric data based univariate density function estimate
zbMATH Open document ID: 0606.62041
DOI: 10.1016/0167-9473(87)90002-8
author: Russell F. Kappenman
published in: Computational Statistics and Data Analysis
publication date: 1987-01-01T00:00:00Z
review text: A method for assigning a value to the smoothing parameter in the kernel estimate of a univariate density function is derived. The method uses observations of the random variable whose probability density function is to be estimated, and it is not sensitive to outliers in the data. Simulation studies were performed to compare the performance of the method with others that have been proposed. These studies indicate that the method produces density estimators which have smaller mean integrated squared errors for a wide variety of types of density functions.
Mathematics Subject Classification ID: 62G05
zbMATH DE Number: 3980244
zbMATH Keywords: kernel density function estimate
zbMATH Keywords: smoothing parameter
zbMATH Keywords: outliers
zbMATH Keywords: Simulation studies
zbMATH Keywords: mean integrated squared errors
MaRDI profile type: Publication
full work available at URL: 0167-9473(87)90002-8
OpenAlex ID: W2074920176
cites work: Monte Carlo Study of Three Data-Based Nonparametric Probability Density Estimators
cites work: Kernel density estimation revisited
cites work: Q4727203
description: scientific article; zbMATH DE number 3980244