82 citations · 149 across the 15 of their papers we have counts for
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stat.ME2018
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…
stat.ME2018★ 9 cited
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…
stat.ME2018★ 7 cited
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…