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
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
Video: