숙명여자대학교 통계학과

 Recent advances in Survival Analysis: Dependency and Deep Learning (5/22 13:30~18:00 젬마홀)

Emura: Flexible bivariate copula models for bivariate survival times under censoring, competing risks, cure, and frailty.

하일도: An M-spline-based unified joint frailty modelling framework for clustered bivariate survival data

최상범: Identifiability and inference of semiparametric copula-based quantile regression under dependent censoring

김양진: Survival forest for current status data with dependent censoring

이지현: A Deep Gaussian Process for Survival Prediction under the Accelerated Failure Time Model

최태화: Regression analysis of multivariate interval-censored data via marginal linear models

이주영: A Multi-State IPCW Framework for Treatment Switching in Randomized Oncology Trials