报告题目：Smoothing parameter selection in two frameworks for penalized splines

报告人： Professor Tatyana Krivobokova

(德国Göttingen大学教授）

摘要：In contrast to other nonparameteric regression techniques, smoothing parameter selection for spline estimators can be performed not only by employing criteria that approximate the average mean squared error (e.g. generalized cross validation), but also by making use of the maximum likelihood (or empirical Bayes) paradigm. In the later case, the function to be estimated is assumed to be a realization of some stochastic process, rather than from a certain class of smooth functions. Under this assumption both smoothing parameter selectors for spline estimators are well-studied and known to perform similar. A more interesting problem is the properties of smoothing parameter estimators in the frequentist framework, that is if the underlying function is non-random. In this talk we discuss the asymptotic properties of both smoothing parameter selection criteria for general low-rank (or so-called penalized) spline smoothers in the frequentist framework and give also insights into their small sample performance.

时间：2012年5月24日（周四）上午9:30开始

地点：数学系致远楼 102会议室

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