2 code implementations • 13 Dec 2019 • Michele Zemplenyi, Mark J. Meyer, Andres Cardenas, Marie-France Hivert, Sheryl L. Rifas-Shiman, Heike Gibson, Itai Kloog, Joel Schwartz, Emily Oken, Dawn L. DeMeo, Diane R. Gold, Brent A. Coull
The ability to identify time periods when individuals are most susceptible to exposures, as well as the biological mechanisms through which these exposures act, is of great public health interest.
Applications Methodology
2 code implementations • 2 Apr 2018 • Yan Wang, Nathan Palmer, Qian Di, Joel Schwartz, Isaac Kohane, Tianxi Cai
We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size $n_0$ is exceedingly large and the covariate dimension $p$ is not small but $n_0\gg p$.
Computation Applications
