Multiple Imputation in Mixture Models for Nonignorable Nonresponse with Follow-ups
作者:
RobertJ. Glynn,
NanM. Laird,
DonaldB. Rubin,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1993)
卷期:
Volume 88,
issue 423
页码: 984-993
ISSN:0162-1459
年代: 1993
DOI:10.1080/01621459.1993.10476366
出版商: Taylor & Francis Group
关键词: Follow-up surveys;Linear models;Maximum likelihood;Missing values
数据来源: Taylor
摘要:
One approach to inference for means or linear regression parameters when the outcome is subject to nonignorable nonresponse is mixture modeling. Mixture models assume separate parameters for respondents and nonrespondents; implementation by multiple imputation consists of repeatedly filling in missing values for nonrespondents, estimating parameters using the filled-in data, and then adjusting for variability between imputations. We evaluated the performance of this scheme using simulated data with a 25% sample of nonrespondents followed up. We conclude that it provides a generally satisfactory and robust approach to inference for means and regression parameters in this case, although a greater number of imputations may be required for good performance compared to the number required for estimation when nonresponse is ignorable.
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