3 papers
stat.CO2018
A Fast Divide-and-Conquer Sparse Cox Regression
Yan Wang, Nathan Palmer, Qian Di +3
We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size is exceedin…
cs.CL2018
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Andrew L. Beam, Benjamin Kompa, Allen Schmaltz +6
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of…
stat.ME2016
Doubly robust matching estimators for high dimensional confounding adjustment
Joseph Antonelli, Matthew Cefalu, Nathan Palmer +1
Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, t…