Estimation for a Class of Semiparametric Pareto Mixture Densities

Jiali Zheng and Xiyang Wang ()
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Jiali Zheng: Shanghai University of Finance and Economics
Xiyang Wang: Shanghai University of Finance and Economics

Sankhya A: The Indian Journal of Statistics, 2022, vol. 84, issue 2, No 9, 609-627

Abstract: Abstract We study the estimation of a class of semiparametric mixture models, where the models have a symmetric nonparametric component and a parametric component of Pareto distribution with unknown parameters. We establish an estimation procedure by minimizing a criterion function after dealing with the jump point. We study the large sample properties of the proposed estimator, and prove consistency and asymptotic normality of the parameter estimation. For the nonparametric component, bias and variance are derived, and a rule-of-thumb bandwidth selection method is given. Simulation studies demonstrate good performance of the proposed methodology.

Keywords: Semiparametric mixture models; Kernel density estimation; Asymptotics; Pareto density; Statistics (62); Nonparametric inference (Gxx) (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13171-020-00208-1

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