Regression Diagnostics to Detect Nonrandom Missingness in Linear Regression
作者:
JeffreyS. Simonoff,
期刊:
Technometrics
(Taylor Available online 1988)
卷期:
Volume 30,
issue 2
页码: 205-214
ISSN:0040-1706
年代: 1988
DOI:10.1080/00401706.1988.10488368
出版商: Taylor & Francis Group
关键词: Missing data;Missing completely at random;Outlier/leverage point diagnostics;Regression fill-in
数据来源: Taylor
摘要:
Missing data is a common problem in regression analysis. The usual estimation strategies require that the data values be missing completely at random (MCAR); if this is not the case, estimates can be severely biased. In this article it is shown that tests can be constructed based on common regression diagnostics to detect non-MCAR behavior. The construction of these tests and their properties when data are missing in one explanatory variable are detailed. Computer simulations indicate good power to detect various non-MCAR processes. Three examples are presented. Extensions to missing data in more than one explanatory variable and to arbitrary regression models are discussed.
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