Randomization Does Not Justify Logistic Regression
Freedman, David A.
Statist. Sci., Tome 23 (2008) no. 1, p. 237-249 / Harvested from Project Euclid
The logit model is often used to analyze experimental data. However, randomization does not justify the model, so the usual estimators can be inconsistent. A consistent estimator is proposed. Neyman’s non-parametric setup is used as a benchmark. In this setup, each subject has two potential responses, one if treated and the other if untreated; only one of the two responses can be observed. Beside the mathematics, there are simulation results, a brief review of the literature, and some recommendations for practice.
Publié le : 2008-05-15
Classification:  Models,  randomization,  logistic regression,  logit,  average predicted probability
@article{1219339115,
     author = {Freedman, David A.},
     title = {Randomization Does Not Justify Logistic Regression},
     journal = {Statist. Sci.},
     volume = {23},
     number = {1},
     year = {2008},
     pages = { 237-249},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1219339115}
}
Freedman, David A. Randomization Does Not Justify Logistic Regression. Statist. Sci., Tome 23 (2008) no. 1, pp.  237-249. http://gdmltest.u-ga.fr/item/1219339115/