ABSTRACT

This talk covers two seemingly different but related topics: (i) semiparametric fractional 
imputation for missing responses, and (ii) sparse and efficient replication variance 
estimation, both under the context of complex surveys, with the long-term research goal of 
connecting the dots between the two problems. For the first topic, we propose a 
semiparametric fractional imputation method for handling item nonresponses, assuming 
certain baseline auxiliary variables can be observed for all units in the sample.  The 
proposed strategy combines the strengths of conventional single imputation and multiple 
imputation methods, and is easy to implement even with a large number of auxiliary 
variables available, which is typically the case for large scale complex surveys. For the 
second topic, we first show how to produce replication weights based on the method 
outlined in Fay (1984) such that the resulting replication variance estimator is 
algebraically equivalent to the fully efficient linearization variance estimator for any 
given sampling design. We then propose a novel application of the calibration method for 
replication weights to simultaneously achieve efficiency and sparsity in the sense that a 
small number sets of replication weights can produce valid and efficient replication 
variance estimators for key parameters. Our proposed method can be used in conjunction 
with existing resampling techniques for large-scale complex surveys. Simulation results 
and some general discussion on related issues will be presented.

This talk is based on joint work with Jiahua Chen of University of British Columbia and 
Jae Kwang Kim of Iowa State University.