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VERSION:2.0
PRODID:-//Iowa State University CALS LAS Web Team//sites.iastate.edu//EN
BEGIN:VEVENT
UID:20160805T160000-437-www.stat.iastate.edu
DTSTART:20160805T160000Z
SEQUENCE:0
TRANSP:OPAQUE
DTEND:20160805T164500Z
SUMMARY:Guillermo Basulto-Elias\, Computation of kernel deconvolution densi
 ty estimators
CLASS:PUBLIC
DESCRIPTION:In many areas of application\, like medical sciences\, variable
 s of interest are not directly observable and may be measured only in the 
 presence of contaminating errors. These cases are often referred as ``meas
 urement error problems.'' Kernel deconvolution density estimation (KDDE) i
 s an approach for handling such measurement errors\, which consists of sep
 arating out and estimating the density of a target variable from observati
 ons blurred by additive errors.&nbsp\; The method involves an adaptation o
 f kernel density estimation using the Fourier inversion theorem. The resul
 ting estimator requires numerical integration of complicated functions. We
  will talk about how to efficiently perform KDDE in R under several sampli
 ng scenarios for univariate and bivariate samples.\n\nMore information at:
  https://www.stat.iastate.edu/event/2016/guillermo-basulto-elias-computati
 on-kernel-deconvolution-density-estimators
DTSTAMP:20260411T181406Z
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