Penalized semiparametric density estimation |
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Authors: | Ying Yang |
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Institution: | (1) Department of Statistics, Colorado State University, Fort Collins, CO 80523-1877, USA;(2) Department of Statistics, Seoul National University, San 56-1, Sillim-dong, Gwanak-gu, Seoul, 151-747, South Korea |
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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. |
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Keywords: | |
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