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Abstract Details
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Activity Number:
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253
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305261 |
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Title:
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The Hosmer-Lemeshow Goodness-of-Fit Test: Does the Grouping Really Matter?
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Author(s):
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Hillary Rivera*+ and Keith Williams and Zoran Bursac and Dave Hosmer
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Companies:
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University of Arkansas for Medical Sciences and University of Arkansas for Medical Sciences and University of Arkansas for Medical Sciences and University of Massachusetts
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Address:
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, , ,
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Keywords:
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Logistic Regression ;
Goodness-of-Fit ;
Hosmer-Lemeshow Goodness of Fit
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Abstract:
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The Hosmer and Lemeshow goodness-of-fit statistic (HLGOF) is a well established method of assessing model fit for logistic regression. This statistic is based on the grouping of predicted probabilities. It is available in many software packages and routines that offer logistic regression modeling. Some suggest that the performance of HLGOF depends on the decile grouping that the test statistic is based on. We propose a modification to HLGOF decile risk approach via random grouping method. In this project we describe our approach and discuss its performance through simulations.
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