Multiply robust imputation procedures for zero-inflated distributions in surveys.

IF 0.7 Q3 STATISTICS & PROBABILITY
Sixia Chen, David Haziza
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引用次数: 6

Abstract

Item nonresponse in surveys is usually treated by some form of single imputation. In practice, the survey variable subject to missing values may exhibit a large number of zero-valued observations. In this paper, we propose multiply robust imputation procedures for treating this type of variable. Our procedures may be based on multiple imputation models and/or multiple nonresponse models. An imputation procedure is said to be multiply robust if the resulting estimator is consistent when all models but one are misspecified. The variance of the imputed estimators is estimated through a generalized jackknife variance estimation procedure. Results from a simulation study suggest that the proposed procedures perform well in terms of bias, efficiency and coverage rate.

在调查中为零膨胀分布增加稳健的估算程序。
调查中的项目无反应通常用某种形式的单一归算来处理。在实践中,受缺失值影响的调查变量可能表现出大量的零值观测值。在本文中,我们提出了处理这类变量的多重鲁棒输入程序。我们的程序可能基于多个输入模型和/或多个无响应模型。如果在除一个模型外的所有模型都被错误指定的情况下,所得到的估计量是一致的,那么我们就说一个估计过程是多重鲁棒的。利用广义折刀方差估计方法估计了估计量的方差。模拟研究结果表明,所提出的程序在偏差、效率和覆盖率方面表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Metron-International Journal of Statistics
Metron-International Journal of Statistics STATISTICS & PROBABILITY-
CiteScore
1.60
自引率
0.00%
发文量
11
期刊介绍: METRON welcomes original articles on statistical methodology, statistical applications, or discussions of results achieved by statistical methods in different branches of science.
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