Robust Sensitivity Analysis for Quantiles of Individual Treatment Effects in Paired Observational Studies
advised by Xinran Li, University of Illinois at Urbana-Champaign (Ongoing)
- Generalize sensitivity analysis to deal with quantiles of individual treatment effects and quantiles of the hidden biases from all matched sets.
- Develop an adaptive test in observational study, together with simulations showing the robustness compare to the usual tests.
- Developing R package.
Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies
advised by Xinran Li, University of Illinois at Urbana-Champaign (July 2020 – July 2021)
- Generalize Rosenbaum’s conventional framework to conduct sensitivity analysis on quantiles of hidden biases from all matched sets, which are more robust than the maximum.
- Demonstrate that the proposed sensitivity analysis for all quantiles of hidden biases is simultaneously valid and is thus a free lunch added to the conventional sensitivity analysis.
- An R package of our method is available.
- Paper has submitted to Journal of the American Statistical Association.
Simple and Fast Algorithms for Rank Estimation of Right-censored Length-biased Data
advised by Gary Chan, University of Washington (June 2018 – Sept. 2018)
- Implemented a fast algorithm in R to solve an over-identified set of non-smooth and non-monotone log-rank estimating equations based on the left-truncated weight-censored data and backward recurrence time.
Recommendation System on Traditional Chinese Medicine Big Data
advised by Xiaohua Zhou, Peking University (June 2017 – June 2018)
- Implemented the recommendation systems through neighborhood-based and model-based collaborative filtering, and deep learning based techniques such as item embedding, feedforward networks, auto-encoders.
- Constructed hybrid recommendation system based on DeepFM and neighborhood-based model and tested on real data.