On balanced random imputation in surveys

• 成果类型: Article
• 署名作者: Chauvet, G.; Deville, J. -C.; Haziza, D.
• 署名单位: Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Universite de Montreal
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asr011
• 发表日期: 2011
• 页码: 459471
• 关键词: efficient random imputation; variance-estimation; inference

摘要

Random imputation methods are often used in practice because they tend to preserve the distribution of the variable being imputed, which is an important property when the goal is to estimate population quantiles. However, this type of imputation method introduces additional variability, the imputation variance, due to the random selection of residuals. In this paper, we propose a class of random balanced imputation methods under which the imputation variance is eliminated while the distribution of the variable being imputed is preserved. The rationale behind balanced imputation is to select residuals at random so that appropriate constraints are satisfied. We describe an algorithm for selecting the random residuals that can be viewed as an adaptation of the cube algorithm proposed in the context of balanced sampling (Deville & Tille, 2004). Results of a simulation study support our findings.