Bandwidth selection in nonparametric density estimation under dependence: a simulation study
type: Item
instance of: scholarly article
title: Bandwidth selection in nonparametric density estimation under dependence: a simulation study
zbMATH Open document ID: 0942.62037
author: Alejandro Quintela-del-Río
author: Ricardo Cao
author: Juan Manuel Vilar-Fernández
published in: Computational Statistics
publication date: 2000-03-02T00:00:00Z
review text: For kernel density estimators (with Gaussian and Epanechnikov kernels) there are considered methods of data driven bandwidth selection: cross-validation (pseudo-likelihood, least squares, biased and smoothed); plug-in method; non-smoothed and smoothed bootstrap; and double kernel method. The simulated data were drawn from an AR(1) model \(X_t=\rho X_{t-1}+ e_t\), where \(e_t\) are i.i.d. with normal, exponential, double-exponential and Rademacher distributions. A mixture of two normal AR(1) processes and \(X_t=2^{-1} \sum_{k=0}^{j-1} e_{t+k}^2\) are also considered. The mean integrated square and absolute errors (MISE, MIAE) and the mean uniform absolute error (MUAE) are used as measures of the estimators' efficiency. The authors concluded that the smoothed cross-validation and plunging methods present a generally good performance.
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62E20
zbMATH DE Number: 1409662
zbMATH Keywords: kernel density estimation
zbMATH Keywords: bandwidth selection
zbMATH Keywords: dependent data
zbMATH Keywords: cross-validation
zbMATH Keywords: bootstrap estimation
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 1409662