Abstract

http://ssrn.com/abstract=1308322
 


 



Confidence Intervals Based on Survey Data with Nearest Neighbor Imputation


Shao Jun


Shanghai Lixin University of Commerce

Hansheng Wang


Peking University - Guanghua School of Management

November 27, 2008

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

Accepted Paper Series


Not Available For Download

Date posted: December 2, 2008  

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: http://ssrn.com/abstract=1308322

Contact Information

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
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