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20132026
most citedLocal tests for identifying anisotropic diffusion areas in human brain with DTI

3 citations · 3 across the 10 of their papers we have counts for

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7 papers · 1 filter

stat.ME2026

Revisiting dependence in multiple testing: empirical distribution approaches for FDP control

Fangyong Zheng, Pengfei Li, Yuan Jiang +1

Large-scale multiple hypothesis testing is central to the analysis of high-throughput data, where controlling false discoveries is critical. Classical procedures typically rely on…

stat.ME2026

A semiparametric two-sample homogeneity test with nonignorable nonresponse using callback data

Xinyu Wang, Tao Yu, Chunlin Wang +1

Testing the homogeneity of two distributions is fundamental in statistics, but classical procedures may fail under nonignorable nonresponse. In many surveys, callback data record r…

stat.ME2026

Maximum smoothed likelihood method for the combination of multiple diagnostic tests, with application to the ROC estimation

Fangyong Zheng, Pengfei Li, Tao Yu

In medical diagnostics, leveraging multiple biomarkers can significantly improve classification accuracy compared to using a single biomarker. While existing methods based on expon…

stat.ME2024

Receiver operating characteristic curve analysis with non-ignorable missing disease status

Dingding Hu, Tao Yu, Pengfei Li

This article considers the receiver operating characteristic (ROC) curve analysis for medical data with non-ignorable missingness in the disease status. In the framework of the log…

stat.ME2024

Shape-restricted transfer learning analysis for generalized linear regression model

Pengfei Li, Tao Yu, Chixiang Chen +1

Transfer learning has emerged as a highly sought-after and actively pursued research area within the statistical community. The core concept of transfer learning involves leveragin…

stat.ME2021

Maximum profile binomial likelihood estimation for the semiparametric Box--Cox power transformation model

Pengfei Li, Tao Yu, Baojiang Chen +1

The Box--Cox transformation model has been widely applied for many years. The parametric version of this model assumes that the random error follows a parametric distribution, say…