The Annals of Applied Statistics

On regression adjustments in experiments with several treatments

David A. Freedman

Full-text: Open access

Abstract

Regression adjustments are often made to experimental data. Since randomization does not justify the models, bias is likely; nor are the usual variance calculations to be trusted. Here, we evaluate regression adjustments using Neyman’s nonparametric model. Previous results are generalized, and more intuitive proofs are given. A bias term is isolated, and conditions are given for unbiased estimation in finite samples.

Article information

Source
Ann. Appl. Stat. Volume 2, Number 1 (2008), 176-196.

Dates
First available in Project Euclid: 24 March 2008

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1206367817

Digital Object Identifier
doi:10.1214/07-AOAS143

Mathematical Reviews number (MathSciNet)
MR2415599

Zentralblatt MATH identifier
1144.62027

Keywords
Models randomization experiments multiple regression estimation bias balanced designs intention-to-treat

Citation

Freedman, David A. On regression adjustments in experiments with several treatments. Ann. Appl. Stat. 2 (2008), no. 1, 176--196. doi:10.1214/07-AOAS143. https://projecteuclid.org/euclid.aoas/1206367817.


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