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Activity Number: 494
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #309681
Title: Implementation of a Bivariate Deconvolution Approach to Estimate the Joint Distribution of Two Non-Normal Random Variables Observed with Measurement Error
Author(s): Eduardo Trujillo Rivera*+ and Guillermo Basulto-Elias and Alicia Carriquiry
Companies: Statistic Department Iowa State University and Iowa State University and Iowa State University
Keywords: Non-parametric ; Deconvolution ; Noisy data ; Bivariate non-normal data ; Computing
Abstract:

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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