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Statistics and Computing

, 19:355 | Cite as

Penalized semiparametric density estimation

  • Ying YangEmail author
Article

Abstract

In this article we propose a penalized likelihood approach for the semiparametric density model with parametric and nonparametric components. An efficient iterative procedure is proposed for estimation. Approximate generalized maximum likelihood criterion from Bayesian point of view is derived for selecting the smoothing parameter. The finite sample performance of the proposed estimation approach is evaluated through simulation. Two real data examples, suicide study data and Old Faithful geyser data, are analyzed to demonstrate use of the proposed method.

Keywords

Density estimation Penalized likelihood estimation Generalized maximum likelihood criterion Reproducing kernel Hilbert space Smoothing splines 

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Mathematical SciencesTsinghua UniversityBeijingChina

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