Nonparametric Estimation of Mean Functionals with Data Missing at Random
作者:
PhilipE. Cheng,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1994)
卷期:
Volume 89,
issue 425
页码: 81-87
ISSN:0162-1459
年代: 1994
DOI:10.1080/01621459.1994.10476448
出版商: Taylor & Francis Group
关键词: Asymptotic normality;Ignorable treatment assignment;Missing at random;Missing data;Nonparametric kernel regression
数据来源: Taylor
摘要:
This article considers a distribution-free estimation procedure for a basic pattern of missing data that often arises from the wellknown double sampling in survey methodology. Without parametric modeling of the missing mechanism or the joint distribution, kernel regression estimators are used to estimate mean functionals through empirical estimation of the missing pattern. A generalization of the method of Cheng and Wei is verified under the assumption of missing at random. Asymptotic distributions are derived for estimating the mean of the incomplete data and for estimating the mean treatment difference in a nonrandomized observational study. The nonparametric method is compared with a naive pairwise deletion method and a linear regression method via the asymptotic relative efficiencies and a simulation study. The comparison shows that the proposed nonparametric estimators attain reliable performances in general.
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