Abstract Details
Activity Number:
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494
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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Abstract - #309681 |
Title:
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Implementation of a Bivariate Deconvolution Approach to Estimate the Joint Distribution of Two Non-Normal Random Variables Observed with Measurement Error
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Author(s):
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Eduardo Trujillo Rivera*+ and Guillermo Basulto-Elias and Alicia Carriquiry
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Companies:
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Statistic Department Iowa State University and Iowa State University and Iowa State University
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Keywords:
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Non-parametric ;
Deconvolution ;
Noisy data ;
Bivariate non-normal data ;
Computing
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Abstract:
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Replicate observations of 25(OH)D (biomarker for vitamin D status) and iPTH are available on a sample of individuals. We assume that measurements are subject to non-normal measurement error. We estimate the joint density of these bivariate data via non-parametric deconvolution. The estimated density is used to compute statistics of public health interest, such as the proportion of persons in a group with 25(OH)D values below iPTH, or the value of 25(OH)D above which iPTH is approximately constant. We use a bootstrap approach to compute confidence intervals. Several bivariate kernel density estimators for the noisy data and estimators for the characteristic function of the error are compared.
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Authors who are presenting talks have a * after their name.
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