From the 1 of 4 linked papers with an AI index.
4 papers
Adaptive Confidence Sets for Binary Regression without Design Smoothness
P. M. Aronow, Patrick Lopatto
We study honest adaptive confidence sets for the regression function in random-design binary regression under loss. Assuming only known bounds on…
On Rates Attainable under Random Design: A Negative Answer to a Problem of Robins
P. M. Aronow, Patrick Lopatto
The paper proves that in nonparametric regression with random design, the conjectured optimal convergence rate for estimating a constant conditional variance is unattainable, estab…
Undocumented Behavior in the gsynth R package and its Consequences for Three Published Studies
Beniamino Green, P. M. Aronow
Prior to the version 1.3.1 update on CRAN in December 2025, gsynth, a popular R package for estimating Interactive Fixed Effects (IFE) models, could drastically and systematically…
Minimax unbiased estimation for finite populations with bounded outcomes
P. M. Aronow, Patrick Lopatto
We study design-unbiased estimation of the finite-population total when each outcome satisfies known bounds . For any sampling design with inclu…