期刊
ADVANCES IN APPLIED MATHEMATICS
卷 40, 期 2, 页码 180-193出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.aam.2006.12.003
关键词
models; randomization; multiple regression; balance; intention-to-treat
Regression adjustments are often made to experimental data. Since randomization does not justify the models, almost anything can happen. Here, we evaluate results using Neyman's non-parametric model, where each subject has two potential responses, one if treated and the other if untreated. Only one of the two responses is observed. Regression estimates are generally biased, but the bias is small with large samples. Adjustment may improve precision, or make precision worse; standard errors computed according to usual procedures may overstate the precision, or understate, by quite large factors. Asymptotic expansions make these ideas more precise. (c) 2007 Published by Elsevier Inc.
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