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UID:20250705T095834EDT-5633sF6uWT@132.216.98.100
DTSTAMP:20250705T135834Z
DESCRIPTION:Title: Multiply robust imputation procedures for the treatment 
 of item nonresponse in surveys.\n\nAbstract: Every time data are collected
 \, it is virtually certain that we will face the problem of missing data. 
 Missing data are undesirable because they make estimates vulnerable to non
 response bias. In surveys\, it is customary to distinguish unit nonrespons
 e from item nonresponse. The former occurs when no usable information is c
 ollected on a sample unit\, whereas the latter is characterized by the abs
 ence of information limited to some survey variables only. Unit nonrespons
 e is usually handled through weight adjustment procedures methods. Item no
 nresponse is typically treated by some form of single imputation\, whereby
  one replacement value is used to fill in for the missing value. In this p
 resentation\, we will describe multiply robust imputation procedures in fi
 nite population sampling. In practice\, multiple nonresponse models and mu
 ltiple imputation models may be fitted\, each involving different subsets 
 of covariates and possibly different link functions. An imputation procedu
 re is said to be multiply robust if the resulting estimator is consistent 
 when all models but one are misspecified. Variance estimation and other ex
 tensions will be discussed. Results from a simulation study will be presen
 ted.www.davidhaziza.com\n
DTSTART:20190911T193000Z
DTEND:20190911T203000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:David Haziza\, PhD\, Université de Montréal
URL:/mathstat/channels/event/david-haziza-phd-universi
 te-de-montreal-300481
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