This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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500
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section for Statistical Programmers and Analysts
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Abstract - #307332 |
Title:
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Adjusted MSE Sequential Method
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Author(s):
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Limin Yang*+ and Li Yang
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Companies:
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Fiserv and Renmin University of China
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Address:
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Haidian Qu, Dongguanchun st. #59, Beijing, International, 100872, P.R.China
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Keywords:
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Variable selection ;
SAS phreg ;
Stepwise ;
Collinearity ;
Modeling
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Abstract:
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Variable selection is a common problem in modeling, especially for financial data. Having several hundred of higher correlated or collinear variables is not unusual. This makes it difficult to select more important variables into a model. Although many works have been done in the area, the problem is far from being solved in practice. This paper suggests an adjusted MSE sequential method, which sequentially excludes and includes variables based on their contributions to the MSE adjusted with collinear factor. By considering both their collinearity and contribution to the model, we can keep more important and less collinear variables. There are four steps: stepwise selection; variable excluding loop; variable including loop; model finalization. An example is shown and it confirmed that this method did select better variables and produced an improved model than just using proc phreg stepwi
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Authors who are presenting talks have a * after their name.
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