8 citations · 22 across the 9 of their papers we have counts for
10 papers
Evaluating the Robustness of Targeted Maximum Likelihood Estimators via Realistic Simulations in Nutrition Intervention Trials
Haodong Li, Sonali Rosete, Jeremy Coyle +9
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, dou…
Personalized Online Machine Learning
Ivana Malenica, Rachael V. Phillips, Romain Pirracchio +3
In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degre…
General order adjusted Edgeworth expansions for generalized -tests
Inna Gerlovina, Alan E. Hubbard
We develop generalized approach to obtaining Edgeworth expansions for -statistics of an arbitrary order using computer algebra and combinatorial algorithms. To incorporate vario…
Estimation of population size based on capture recapture designs and evaluation of the estimation reliability
Yue You, Mark van der Laan, Philip Collender +6
We propose a modern method to estimate population size based on capture-recapture designs of K samples. The observed data is formulated as a sample of n i.i.d. K-dimensional vector…
Targeting Learning: Robust Statistics for Reproducible Research
Jeremy R. Coyle, Nima S. Hejazi, Ivana Malenica +9
Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions w…
A Data-Adaptive Targeted Learning Approach of Evaluating Viscoelastic Assay Driven Trauma Treatment Protocols
Linqing Wei, Lucy Z. Kornblith, Alan Hubbard
Estimating the impact of trauma treatment protocols is complicated by the high dimensional yet finite sample nature of trauma data collected from observational studies. Viscoelasti…
A Fundamental Measure of Treatment Effect Heterogeneity
Jonathan Levy, Mark van der Laan, Alan Hubbard +1
We offer a non-parametric plug-in estimator for an important measure of treatment effect variability and provide minimum conditions under which the estimator is asymptotically effi…
A machine learning-based approach for estimating and testing associations with multivariate outcomes
David Benkeser, Andrew Mertens, Benjamin F. Arnold +5
We propose a method for summarizing the strength of association between a set of variables and a multivariate outcome. Classical summary measures are appropriate when linear relati…
A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology
Nima S. Hejazi, Philippe Boileau, Mark J. van der Laan +1
The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational biology an…
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings
Weixin Cai, Nima S. Hejazi, Alan E. Hubbard
Current statistical inference problems in areas like astronomy, genomics, and marketing routinely involve the simultaneous testing of thousands -- even millions -- of null hypothes…