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Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimator

Collection
  • Cahiers de recherche; 2003.20
Publication date2003
Abstract

We consider semiparametric asymmetric kernel density estimators when the unknown density has support on [ 0, infinity) We provide a unifying framework which contains asymmetric kernel versions of several semiparametric density estimators considered previously in the literature This framework allows us to use popular parametric models in a nonparametric fashion and yields estimators which are robust to misspecification We further develop a specification test to determine if a density belongs to a particular parametric family The proposed estimators outperform rival non- and semiparametric estimators in finite samples and are simple to implement We provide applications to loss data from a large Swiss health insurer and Brazilian income data.

Keywords
  • Semiparametric density estimation
  • Asymmetric kernel
  • Income distribution
  • Loss distribution
  • Health insurance
  • Specification testing
Classification
  • JEL : C13, C14
Citation (ISO format)
HAGMANN, Matthias, SCAILLET, Olivier. Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimator. 2003
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Identifiers
  • PID : unige:5788
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Technical informations

Creation04/15/2010 12:20:07 PM
First validation04/15/2010 12:20:07 PM
Update time03/14/2023 3:26:35 PM
Status update03/14/2023 3:26:35 PM
Last indexation05/02/2024 11:31:59 AM
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