|
Activity Number:
|
115
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Monday, August 1, 2016 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Government Statistics Section
|
|
Abstract #319343
|
|
|
Title:
|
Semiparametric Fractional Imputation Using Empirical Likelihood in Survey Sampling
|
|
Author(s):
|
Sixia Chen* and Jae-kwang Kim
|
|
Companies:
|
University of Oklahoma and Iowa State University
|
|
Keywords:
|
Item nonresponse ;
Missing data ;
Quantile estimation ;
Robust estimation
|
|
Abstract:
|
Empirical likelihood method is a powerful tool for incorporating moment conditions in statistical inference. We propose a novel application of the empirical likelihood for handling item nonresponse in survey sampling. The proposed method takes the form of fractional imputation (Kim, 2011) but it does not require parametric model assumptions. Instead, only the first moment condition based on regression model is used and the empirical likelihood method is applied to the observed residuals to get the fractional weights. The resulting semiparametric fractional imputation provides root n consistent estimates for various parameters. Variance estimation is implemented using a jackknife method. The proposed method is applied to impute for Systolic blood pressure variable in 2013-2014 National Health and Nutrition Examination Survey (NHANES) data.
|
Authors who are presenting talks have a * after their name.
