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TZID:Europe/Paris
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DTSTART:20251026T030000
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UID:calendar.137270.field_date.0@www.tse-fr.eu
DTSTAMP:20260614T195131Z
CREATED:20250430T181001Z
DESCRIPTION:Alexander Meister (Rostock University)\, “Multivariate root-n-c
 onsistent smoothing parameter free matching estimators and estimators of i
 nverse density weighted expectations”\, MAD-Stat. Seminar\, Toulouse: TSE\
 , June 5\, 2025\, 11:00–12:15\, room Auditorium 3.\n\n: Expected values we
 ighted by the inverse of a multivariate density or\, equivalently\, Lebesg
 ue integrals of regression functions with multivariate regressors occur in
  various areas of applications\, including estimating average treatment ef
 fects\, nonparametric estimators in random coefficient regression models o
 r deconvolution estimators in Berkson errors-in-variables models. The freq
 uently used nearest-neighbor and matching estimators\nsuffer from bias pro
 blems in multiple dimensions. By using polynomial least squares fits on ea
 ch cell of the Kth-order Voronoi tessellation for sufficiently large K\, w
 e develop novel modifications of nearest-neighbor and matching estimators 
 which again converge at the parametric root-n-rate under mild smoothness a
 ssumptions on the unknown regression function and\nwithout any smoothness 
 conditions on the unknown density of the covariates. We stress that in con
 trast to competing methods for correcting for the bias of matching estimat
 ors\, our estimators do not involve nonparametric function estimators and 
 in particular do not rely on sample-size dependent smoothing parameters. W
 e complement the upper bounds with appropriate lower bounds derived from i
 nformation-theoretic arguments\, which show that some smoothness of the re
 gression function is indeed required to achieve the parametric rate. Simul
 ations illustrate the practical feasibility of the proposed methods. This 
 talk is based on a joint work with Hajo Holzmann (Philipps-University of M
 arburg\, Germany).
DTSTART;TZID=Europe/Paris:20250605T120000
DTEND;TZID=Europe/Paris:20250605T131500
LAST-MODIFIED:20251022T001001Z
LOCATION:Toulouse: TSE\, June 5\, 2025\, 11:00–12:15\, room Auditorium 3
SUMMARY:MAD-Stat. Seminar
URL;TYPE=URI:https://www.tse-fr.eu/seminars/2025-multivariate-root-n-consis
 tent-smoothing-parameter-free-matching-estimators-and-estimators-inverse
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