82 citations · 115 across the 12 of their papers we have counts for
12 papers
Adaptive Data-Borrowing for Improving Treatment Effect Estimation using External Controls
Qinwei Yang, Jingyi Li, Peng Wu
Randomized controlled trials (RCTs) often exhibit limited inferential efficiency in estimating treatment effects due to small sample sizes. In recent years, the combination of exte…
Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
Jin Zhu, Jingyi Li, Hongyi Zhou +3
This paper focuses on the design of spatial experiments to optimize the amount of information derived from the experimental data and enhance the accuracy of the resulting causal ef…
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data
Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li +1
COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing…
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification
Chihao Zhang, Yiling Elaine Chen, Shihua Zhang +1
Outcome labeling ambiguity and subjectivity are ubiquitous in real-world datasets. While practitioners commonly combine ambiguous outcome labels for all data points (instances) in…
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification
Wei Vivian Li, Xin Tong, Jingyi Jessica Li
Asymmetric binary classification problems, in which the type I and II errors have unequal severity, are ubiquitous in real-world applications. To handle such asymmetry, researchers…
Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines
Jingyi Jessica Li, Xin Tong
Making binary decisions is a common data analytical task in scientific research and industrial applications. In data sciences, there are two related but distinct strategies: hypoth…
A flexible model-free prediction-based framework for feature ranking
Jingyi Jessica Li, Yiling Chen, Xin Tong
Despite the availability of numerous statistical and machine learning tools for joint feature modeling, many scientists investigate features marginally, i.e., one feature at a time…
Generalized Pearson correlation squares for capturing mixtures of bivariate linear dependences
Jingyi Jessica Li, Xin Tong, Peter J. Bickel
Motivated by the pressing needs for capturing complex but interpretable variable relationships in scientific research, here we generalize the squared Pearson correlation to capture…
A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models
Hanzhong Liu, Xin Xu, Jingyi Jessica Li
Constructing confidence intervals for the coefficients of high-dimensional sparse linear models remains a challenge, mainly because of the complicated limiting distributions of the…
Matched bipartite block model with covariates
Zahra S. Razaee, Arash A. Amini, Jingyi Jessica Li
Community detection or clustering is a fundamental task in the analysis of network data. Many real networks have a bipartite structure which makes community detection challenging.…
Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristics (NP-ROC)
Xin Tong, Yang Feng, Jingyi Jessica Li
In many binary classification applications such as disease diagnosis and spam detection, practitioners often face great needs to control type I errors (i.e., the conditional probab…
MSIQ: Joint Modeling of Multiple RNA-seq Samples for Accurate Isoform Quantification
Wei Vivian Li, Anqi Zhao, Shihua Zhang +1
Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into ge…