1 code implementation • 26 May 2022 • Tim Pearce, Jong-Hyeon Jeong, Yichen Jia, Jun Zhu
To offer theoretical insight into our algorithm, we show firstly that it can be interpreted as a form of expectation-maximisation, and secondly that it exhibits a desirable `self-correcting' property.
1 code implementation • 8 Feb 2022 • Jong-Hyeon Jeong, Yichen Jia
With the rapid advances of deep learning, many computational methods have been developed to analyze nonlinear and complex right censored data via deep learning approaches.
1 code implementation • 14 Jul 2020 • Yichen Jia, Jong-Hyeon Jeong
This paper presents a novel application of the neural network to the quantile regression for survival data with right censoring, which is adjusted by the inverse of the estimated censoring distribution in the check function.