Penalized semiparametric density estimation
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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.
KeywordsDensity estimation Penalized likelihood estimation Generalized maximum likelihood criterion Reproducing kernel Hilbert space Smoothing splines
- Copas, J.B., Fryer, M.J.: Density estimation and suicide risks in psychiatric treatment. J. R. Stat. Soc. A 143 (1980) Google Scholar
- Eggermont, P.P.B., Lariccia, V.N.: Maximum Penalized Likelihood Estimation, vol. I: Density Estimation. Springer, New York (2001) Google Scholar
- Wahba, G.: Spline Modeling for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 59. SIAM, Philadelphia (1990) Google Scholar