清華大學、交通大學
統 計 學 研 究 所
專 題 演 講
題 目: Model Selection Using Generalized Degrees of Freedom
主講人: 黃信誠博士 (中央研究院 統計科學研究所)
時 間: 96年5月11日（星期五）10：40 - 11：30
（上午10：20-10：40茶會於統計所821室舉行）
地 點: 清大綜合三館837室
Abstract
Model selection is important in many scientific and engineering problems.
In the literature, model selection in the context of nonGaussian distributions
or among nonlinear procedures has not yet received a lot of attention. In this
talk, a general technique of model assessment based on generalized degrees of
freedom (GDF) is introduced and a formula of unbiased risk estimation is
derived, which leads to a methodology of estimating GDF via data perturbation.
The methodology is allowed to compare arbitrary complex methods regardless of
whether the candidate methods are parametric or nonparametric, and whether the
estimates are linear or nonlinear. Some numerical examples on geostatistical
models and Poisson regression models will be provided, and some theoretical
justification will be given to demonstrate the effectiveness of the proposed
methodology.
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