On regression adjustments to experimental data
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...
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| Published in: | Advances in applied mathematics Vol. 40; no. 2; pp. 180 - 193 |
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| Format: | Journal Article |
| Language: | English |
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San Diego, CA
Elsevier Inc
01.02.2008
Elsevier |
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| ISSN: | 0196-8858, 1090-2074 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Freedman, David A. |
| Author_xml | – sequence: 1 givenname: David A. surname: Freedman fullname: Freedman, David A. email: freedman@stat.berkeley.edu organization: Statistics Department, University of California, Berkeley, CA 94720-3860, USA |
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| Snippet | Regression adjustments are often made to experimental data. Since randomization does not justify the models, almost anything can happen. Here, we evaluate... |
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| SubjectTerms | Balance Exact sciences and technology General mathematics General, history and biography Intention-to-treat Mathematics Multiple regression Numerical analysis Numerical analysis. Scientific computation Randomization Sciences and techniques of general use |
| Title | On regression adjustments to experimental data |
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