BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20250918T093913EDT-6628OhhCUB@132.216.98.100
DTSTAMP:20250918T133913Z
DESCRIPTION:Title:\n\nStatistical inference in surveys with random forest i
 mputed estimators\n\nAbstract: \n\nSurvey sampling is concerned with the e
 stimation of finite population parameters. Most often\, the survey variabl
 e is only partially observed due to missing data. In surveys\, item nonres
 ponse is usually handled through some form of imputation\, a procedure con
 sisting of replacing missing values with predicted values. In recent years
 \, imputation through machine learning procedures has attracted a lot of a
 ttention in national statistical offices. However\, little is known about 
 the theoretical properties of the resulting point estimators. In this talk
 \, we will investigate the properties of regression trees and random fores
 ts imputed estimators in surveys. The asymptotic properties of these estim
 ators will be discussed. Variance estimation will be investigated: we will
  show that traditional variance estimators may be biased for some configur
 ation of hyper-parameters and suggest a novel variance estimator based on 
 a K-fold cross-validation procedure. A simulation study will be presented 
 to assess the performances of the proposed point and variance estimators. 
 Finally\, the choice of hyper-parameters in random forest algorithms will 
 be discussed through a mix of theoretical and empirical results.\n
DTSTART:20230918T200000Z
DTEND:20230918T210000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Mehdi Dagdoug (9IÖÆ×÷³§Ãâ·Ñ)
URL:/mathstat/channels/event/mehdi-dagdoug-mcgill-univ
 ersity-350551
END:VEVENT
END:VCALENDAR