JSM 2005 - Toronto

Abstract #303316

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 486
Type: Contributed
Date/Time: Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #303316
Title: Model Choice: Universal Principles and Approximation
Author(s): Henrike Weinert*+ and Daniel J. Nordman and Ursula Gather and Laurie Davies
Companies: University of Dortmund and University of Wisconsin, La Crosse and University of Dortmund and University of Duisburg-Essen
Address: Vogelpothsweg 87, Dortmund, 44221, Germany
Keywords: approximation ; model choice ; Taut-string-method ; MDL-methods
Abstract:

We consider two approaches to model choice. The first is based on the use of universal principles by which we mean the final choice of model is based on some principle, no matter the subject of the data (e.g., physics or medicine). The second is a data approximation approach. This approach chooses the model based on particular features of the data. Such features could be peaks, autocorrelation structure, or variability. They depend on the subject matter under investigation. A further difference is that most universal principles search for a real underlying function in the data, whereas the second approach tries to find a "satisfying approximation" of the data, where satisfying has to be specified. Our comparison includes simulation studies and analysis of real data examples in the area of nonparametric regression. In particular, the Taut-string method of Davies and Kovac (2001, 2004), a method of data approximation, is compared with other methods derived from universal principles such as wavelet- and kernel-methods and Minimum-Description-Length or Normalized-Maximum-Likelihood, respectively.

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