Adjusted jackknife for imputation under unequal probability sampling without replacement

• 成果类型: Article
• 署名作者: Berger, Yves G.; Rao, J. N. K.
• 署名单位: University of Reading; Carleton University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2006.00555.x
• 发表日期: 2006
• 页码: 531-547
• 关键词: variance-estimation; estimator

摘要

Imputation is commonly used to compensate for item non-response in sample surveys. If we treat the imputed values as if they are true values, and then compute the variance estimates by using standard methods, such as the jackknife, we can seriously underestimate the true variances. We propose a modified jackknife variance estimator which is defined for any without-replacement unequal probability sampling design in the presence of imputation and non-negligible sampling fraction. Mean, ratio and random-imputation methods will be considered. The practical advantage of the method proposed is its breadth of applicability.