17 citations · 57 across the 9 of their papers we have counts for
8 papers · 1 filter
Neyman-Pearson and equal opportunity: when efficiency meets fairness in classification
Jianqing Fan, Xin Tong, Yanhui Wu +2
Organizations often rely on statistical algorithms to make socially and economically impactful decisions. We must address the fairness issues in these important automated decisions…
Inference for linear functionals of high-dimensional longitudinal proteomics data using generalized estimating equations
Lu Xia, Ali Shojaie
Regression analysis of correlated data, where multiple correlated responses are recorded on the same unit, is ubiquitous in many scientific areas. With the advent of new technologi…
Non-splitting Neyman-Pearson Classifiers
Jingming Wang, Lucy Xia, Zhigang Bao +1
The Neyman-Pearson (NP) binary classification paradigm constrains the more severe type of error (e.g., the type I error) under a preferred level while minimizing the other (e.g., t…
Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification
Lucy Xia, Richard Zhao, Yanhui Wu +1
This paper addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimi…
Neyman-Pearson classification: parametrics and sample size requirement
Xin Tong, Lucy Xia, Jiacheng Wang +1
The Neyman-Pearson (NP) paradigm in binary classification seeks classifiers that achieve a minimal type II error while enforcing the prioritized type I error controlled under some…
Unifying approach to selective inference with applications to cross-validation
Jelena Markovic, Lucy Xia, Jonathan Taylor
We develop tools to do valid post-selective inference for a family of model selection procedures, including choosing a model via cross-validated Lasso. The tools apply universally…
A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models
Jianqing Fan, Yang Feng, Lucy Xia
Measuring conditional dependence is an important topic in statistics with broad applications including graphical models. Under a factor model setting, a new conditional dependence…
QUADRO: A supervised dimension reduction method via Rayleigh quotient optimization
Jianqing Fan, Zheng Tracy Ke, Han Liu +1
We propose a novel Rayleigh quotient based sparse quadratic dimension reduction method - named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization) - for analyzing high…