Abstract #302280

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JSM 2003 Abstract #302280
Activity Number: 25
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #302280
Title: Statistical Learning Theory and Statistics: Embracing New Technologies
Author(s): Kevin K. Watanabe*+
Companies: Kxen
Address: 8129 Laurel Run Dr., Charlotte, NC, 28269-6175,
Keywords: statistical ; learning ; theory ; structured ; risk ; minimization
Abstract:

J. Friedman stated in a recent article that as statisticians "we can decide to accommodate or resist change. We may have to moderate our tendency to disregard developments." Statistical Learning Theory (SLT) is one such method that is embraced by computer scientists but viewed suspiciously by traditional statisticians. A foundational theory of SLT is structured risk minimization (SRM). The strengths of SRM theory include the ability to automatically control model fit and model consistency; automate the data preparation phase of modeling which often is time consuming and tedious; and generate target optimized bins for continuous as well as categorical variables. Another significant advantage is the ability to build predictive models with all available variables in a data warehouse. The "curse of dimensionality" is easily overcome using SLT. With the tremendous volume of data being collected daily, new breakthrough technologies such as SLT can became a powerful tool for statisticians. The future of statistics requires the evaluation and embracing of new technologies such as SLT to keep pace with the emerging technologies being developed by the other information sciences.

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