Confidence Intervals Based on Survey Data with Nearest Neighbor Imputation

Statistica Sinica, Vol. 18, pp. 281-297, 2008

Posted: 2 Dec 2008  

Shao Jun

Shanghai Lixin University of Commerce

Hansheng Wang

Peking University - Guanghua School of Management

Date Written: November 27, 2008

Abstract

Nearest neighbor imputation (NNI) is a popular method used to compensate for item nonresponse in sample surveys. Although previous results showed that the NNI sample mean and quantiles are consistent estimators of the population mean and quantiles, large sample inference procedures, such as asymptotic confidence intervals for the population mean and quantiles, are not available. For the population mean, we establish the asymptotic normality of the NNI sample mean and derive a consistent estimator of its limiting variance, which leads to an asymptotically valid confidence interval. For the quantiles, we obtain consistent variance estimators and asymptotically valid confidence intervals using a Bahadur-type representation for NNI sample quantiles. Some limited simulation results are presented to examine the finite-sample performance of the proposed variance estimators and confidence intervals.

Keywords: Bahadur representation, hot deck, mean quantiles, variance estimation

JEL Classification: C5, C59

Suggested Citation

Jun, Shao and Wang, Hansheng, Confidence Intervals Based on Survey Data with Nearest Neighbor Imputation (November 27, 2008). Statistica Sinica, Vol. 18, pp. 281-297, 2008. Available at SSRN: https://ssrn.com/abstract=1308322

Shao Jun

Shanghai Lixin University of Commerce ( email )

2800 Wenxiang Road
Shanghai
China

Hansheng Wang (Contact Author)

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

HOME PAGE: http://hansheng.gsm.pku.edu.cn

Paper statistics

Abstract Views
374