JSM 2005 - Toronto

Abstract #303622

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 199
Type: Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #303622
Title: Componentwise Iterative Optimization for Large Data
Author(s): Yachen Lin*+
Companies: Certegy
Address: 11601 Roosevelt Blvd, Saint Petersburg, FL, 33716, United States
Keywords: Credit Risk Analysis ; Component-wise Iterative Optimization ; Data mining ; Gibbs sampling ; OLAP (On-line analytical processing) ; Transactional Data
Abstract:

As the amount of information recorded and stored electronically grows, there is increasing demand for a better way to process it. In the predictive modeling area, the traditional batch version of estimation methods has shown its limitation, for example in the case of transactional data from the credit card market and check processing business. For a task such as linear regression, if a traditional method were applied, it would generate a huge design matrix and require substantial memory. Even given the computing resources, the method is still virtually impossible to update the model on the fly. Therefore, a method is highly desirable for both solving the barrier of computing resource and updating the model on the fly. Such a method will allow the business to timely adapt to the dynamic of the market. In this paper, I propose a method called CIO-component-wise iterative optimization. It provides a new way to generate estimates for the parameters, and furthermore, to allow to updates of estimates on the fly.

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Revised March 2005