Statistics Across Campuses: David Haziza -- Multiply robust imputation procedures for the treatment of item nonresponse in surveys

Multiply robust imputation procedures for the treatment of item nonresponse in surveys.  

Date: 18 September 2020, Friday 

Time: 10am 

Speaker: Prof David Haziza (University of Ottawa) 

Abstract: 

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
nonresponse bias.  In surveys, it is customary to distinguish unit nonresponse from item
nonresponse.  The former occurs when no usable information is collected on a sample
unit, whereas the latter is characterized by the absence of information limited to some
survey variables only.  Unit nonresponse is usually handled through weight adjustment
procedures methods.  Item nonresponse is typically treated by some form of single
imputation, whereby one replacement value is used to fill in for the missing value.  In
this presentation, we will describe multiply robust imputation procedures in finite
population sampling.  In practice, multiple nonresponse models and multiple imputation
models may be fitted, each involving different subsets of covariates and possibly
different link functions.  An imputation procedure is said to be multiply robust if the
resulting estimator is consistent when all models but one are misspecified.  Variance
estimation and other extensions will be discussed.  Results from a simulation study will
be presented.  

Link: https://au.bbcollab.com/guest/fcf219c74ac743e89565a9e6e8d349a9