From the 1 of 5 linked papers with an AI index.
1 citations · 1 across the 2 of their papers we have counts for
5 papers
Pooling Versus Ensembling for Ridge Regression Under Covariate Shift
Maya Ramchandran, Rajarshi Mukherjee
Datasets in many settings naturally partition into clusters arising from sub-populations, batch effects, or aggregation across multiple sources. A common response to such heterogen…
Cross-Cluster Weighted Forests
Maya Ramchandran, Rajarshi Mukherjee, Giovanni Parmigiani
The paper introduces Cross-Cluster Weighted Forests, an ensemble method that clusters training data, fits a random forest within each cluster, and combines them using stacked regre…
Nuisance Function Tuning and Sample Splitting for Optimally Estimating a Doubly Robust Functional
Sean McGrath, Rajarshi Mukherjee
Estimators of doubly robust functionals typically rely on estimating two complex nuisance functions, such as the propensity score and conditional outcome mean for the average treat…
Semiparametric Efficient Empirical Higher Order Influence Function Estimators
Lin Liu, Rajarshi Mukherjee, Whitney K. Newey +1
Robins et al. (2008, 2017) applied the theory of higher order influence functions (HOIFs) to derive an estimator of the mean of an outcome Y in a missing data model with Y mis…
PC Adjusted Testing for Low Dimensional Parameters
Sohom Bhattacharya, Rounak Dey, Rajarshi Mukherjee
In this paper, we investigate the impact of high-dimensional Principal Component (PC) adjustments on inferring the effects of variables on outcomes, with a focus on applications in…