Models as Approximations - A Conspiracy of Random Regressors and Model Deviations Against Classical Inference in Regression

By Andreas Buja, Richard A. Berk, Lawrence D. Brown, Edward I. George, E. Pitkin, Mikhail Traskin, Linda H. Zhao, Kai Zhang. 2015

24 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Cynthia Dwork
2. Jing Lei
3. Aaron Roth
4. Richard A. Berk
5. Alessandro Rinaldo
6. Larry A. Wasserman
7. Michael Kearns
8. John C. Duchi

Citations in the corpus

• Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, background): “5), and also can be found in econometrics (White, 1980) and statistics (Freedman, 1981; Buja et al., 2017).”
• Bootstrapping and sample splitting for high-dimensional, assumption-lean inference (2016, methodology): “Buja et al. (2015) have devoted a whole paper to this issue.”
• Working with Misspecified Regression Models (2018, methodology): “We have called the conventional regression formulation “assumption-laden” (Buja et al., 2016).”