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
