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PRODID:-//University of Illinois//Web Services Calendar//EN
VERSION:2.0
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20120214T161129Z
DTSTART;TZID=America/Chicago:20091119T160000
DTEND;TZID=America/Chicago:20091119T160000
SUMMARY:Statistics seminar:``Spline-backfitted kernel smoothing of additi
 ve models" by Jing Wang\, UIC
CREATED:20090407T150000Z
DESCRIPTION:Abstract: A great deal of effort has been devoted to the infe
 rence of additive model in the last decade. Among existing procedures\, 
 the kernel type are too costly to implement for high dimensions or large
  sample sizes\, while the spline type provide no asymptotic distribution
  or uniform convergence. We propose a one step backfitting estimator of 
 the component function in an additive regression model\, using spline es
 timators in the first stage followed by kernel/local linear estimators. 
 Under weak conditions\, the proposed estimator's pointwise distribution 
 is asymptotically equivalent to an univariate kernel/local linear estima
 tor\, hence the dimension is effectively reduced to one at any point. Th
 is dimension reduction holds uniformly over an interval under assumption
 s of normal errors. Monte Carlo evidence supports the asymptotic results
  for dimensions ranging from low to very high\, and sample sizes ranging
  from moderate to large. The proposed confidence band is applied to the 
 Boston housing data for linearity diagnosis.
LAST-MODIFIED:20091109T160000Z
LOCATION:(ROOM CHANGE) 122 Illini Hall
CATEGORIES:Seminar
CONTACT:3-2167
ORGANIZER:office@illinois.edu
URL:http://illinois.edu/calendar/detail/1439?key=2000010120000101115729
UID:115729@illinois.edu
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