Variance estimation when donor imputation is used to fill in missing values

被引:17
作者
Beaumont, Jean-Francois [1 ]
Bocci, Cynthia [2 ]
机构
[1] STAT Canada, Stat Res & Innovat Div, Ottawa, ON K1A 0T6, Canada
[2] STAT Canada, Business Survey Methods Div, Ottawa, ON K1A 0T6, Canada
来源
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2009年 / 37卷 / 03期
关键词
Hot-Deck imputation; Imputation model; Nearest-neighbour imputation; Nonresponse variance component; SEVANI; Smoothing splines; HOT DECK IMPUTATION;
D O I
10.1002/cjs.10019
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Donor imputation is frequently used in Surveys. However, very few variance estimation methods that take into account donor imputation have been developed in the literature. This is particularly true for surveys with high sampling fractions using nearest donor imputation, often called nearest-neighbour imputation. In this paper, the authors develop a variance estimator for donor imputation based on the assumption that the imputed estimator of a domain total is approximately unbiased under an imputation models that is, a model for the variable requiring-imputation. Their variance estimator is valid, irrespective of the magnitude of the sampling fractions and the complexity of the donor imputation method, provided that the imputation model mean and variance are accurately estimated. They evaluate its performance in a simulation study and show that nonparametric estimation of the model mean and variance via smoothing splines brings robustness with respect to imputation model misspecifications. They also apply their variance estimator to real survey data when nearest-neighbour imputation has been used to fill in the missing values. The Canadian Journal of statistics 37: 400-416; 2009 (C) 2009 Statistical Society of Canada
引用
收藏
页码:400 / 416
页数:17
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