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Activity Number:
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533
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #309166 |
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Title:
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Estimating the Distribution Function with Measurement Error in a Complicated Survey
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Author(s):
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Liang Li*+ and Tom Greene
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Companies:
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The Cleveland Clinic and The Cleveland Clinic
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Address:
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9500 Euclid Ave Wb4, Cleveland, OH, 44195,
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Keywords:
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chronic renal disease ; density estimation ; iSIMEX ; measurement error ; NHANES ; SIMEX
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Abstract:
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The simulation extrapolation (SIMEX) method can be used to estimate the cumulative density function of a random variable that is unobservable and measured with additive error (Stefanski and Bay, 1996). Motivated by a study using the NHANES survey, we extend the SIMEX methodology by adding iterative bootstrap bias reduction steps. This iterative SIMEX (iSIMEX) method is shown to have less bias and mean square error than the SIMEX in our problem, and the resulting cumulative density function is monotone and constrained between 0 and 1. We apply the proposed methodology to estimating the population prevalence of people with various levels of chronic renal disease, and population distribution of kidney function in the US.
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