Density deconvolution for generalized skew-symmetric distributions

Cornelis J. Potgieter ()
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Cornelis J. Potgieter: Department of Mathematics, Texas Christian University

Journal of Statistical Distributions and Applications, 2020, vol. 7, issue 1, 1-20

Abstract: 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.

Keywords: Characteristic function; Kernel methods; Measurement error; Method of moments; Semiparametric estimation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1186/s40488-020-00103-y

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