Abstract #300392

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JSM 2003 Abstract #300392
Activity Number: 357
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #300392
Title: Model Selections
Author(s): Chih-Ling Tsai*+
Companies: University of California
Address: Graduate School of Management, Davis, CA, 95616,
Keywords: Akaike information criterion ; mixture regression model ; single-index model ; survival model
Abstract:

One of the fundamental problems in data analysis is that of selecting an appropriate model from a potential class of candidate models to apply in a particular situation of practical interest. Hence the focus of most studies in selection is on a single task (e.g., variable selections, bandwidth selection, autoregressive order selection, and clustering selection). We discuss selection criteria which can be used in multiple tasks. Specifically, we study the problems of jointly selecting the number of components and variables in finite mixture regression models, simultaneously selecting regression and autoregressive variables in autoregressive regression models, contemporarily choosing variables and bandwidth in single-index models, and concurrently choosing variables and family of distribution in parametric survival models.

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