Loading...
Density deconvolution for generalized skew-symmetric distributions
Potgieter, Cornelis J.
Potgieter, Cornelis J.
Citations
Altmetric:
Soloist
Composer
Publisher
SpringerNature
Date
2020-07-23
Additional date(s)
Abstract
The density deconvolution problem is considered for random variables assumed to belong to the generalized skew-symmetric (GSS) family of distributions. The approach is semiparametric in that the symmetric component of the GSS distribution is assumed known, and the skewing function capturing deviation from the symmetric component is estimated using a deconvolution kernel approach. This requires the specification of a bandwidth parameter. The mean integrated square error (MISE) of the GSS deconvolution estimator is derived, and two bandwidth estimation methods based on approximating the MISE are also proposed. A generalized method of moments approach is also developed for estimation of the underlying GSS location and scale parameters. Simulation study results are presented including a comparing the GSS approach to the nonparametric deconvolution estimator. For most simulation settings considered, the GSS estimator is seen to have performance superior to the nonparametric estimator.
Contents
Subject
Characteristic function
Kernel methods
Measurement error
Method of moments
Semiparametric estimation
Kernel methods
Measurement error
Method of moments
Semiparametric estimation
Subject(s)
Research Projects
Organizational Units
Journal Issue
Genre
Description
Format
Department
Mathematics