5 citations · 15 across the 9 of their papers we have counts for
9 papers
Comparison of statistical methods for high-dimensional compositional data from flow cytometry: A critical perspective on log-ratio transformation and differential abundance testing
Jong-Hyeon Jeong
Flow cytometry generates inherently compositional count data: observed cell population counts are constrained to sum to the total number of acquired events, which precludes direct…
Recovering the Target Hazard Ratio Under Nonproportional Hazards Induced by an Omitted Covariate: Simulation-based Approach
Jong-Hyeon Jeong
When an omitted covariate whose inclusion would balance proportional hazards is excluded from a proportional hazards model, bias in the estimated treatment effect may arise. The om…
Causal Inference on Win Ratio for Observational Data with Dependent Subjects
Di Zhang, Stephen R. Wisniewski, Jong-Hyeon Jeong
Composite endpoints are commonly used with an anticipation that clinically relevant endpoints as a whole would yield meaningful treatment benefits. The win ratio is a rank-based st…
Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis
Tim Pearce, Jong-Hyeon Jeong, Yichen Jia +1
This paper considers doing quantile regression on censored data using neural networks (NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target…
CausalDeepCENT: Deep Learning for Causal Prediction of Individual Event Times
Jong-Hyeon Jeong, Yichen Jia
Deep learning (DL) has recently drawn much attention in image analysis, natural language process, and high-dimensional medical data analysis. Under the causal direct acyclic graph…
DeepCENT: Prediction of Censored Event Time via Deep Learning
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. However,…
Deep Learning for Quantile Regression under Right Censoring: DeepQuantreg
Yichen Jia, Jong-Hyeon Jeong
The computational prediction algorithm of neural network, or deep learning, has drawn much attention recently in statistics as well as in image recognition and natural language pro…
Quantile regression on inactivity time
Lauren C. Balmert, Ruosha Li, Limin Peng +1
The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a…
Domain of Inverse Double Arcsine Transformation
Jong-Hyeon Jeong
To combine the proportions from different studies for meta-analysis, Freeman and Tukey double arcsine tranformation can be useful for normalization and variance stabilization. The…