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Siyan Liu

5 papers hereh-index 217 citations7 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author1

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • stat.ME4
  • stat.ML1
same name
  • Siyan Liu — 3 papers, h 1
  • Siyan Liu — 2 papers, h 2
  • Siyan Liu — 2 papers, h 5
  • Siyan Liu — 1 paper, h 10
  • Siyan Liu — 1 paper, h 2
  • Siyan Liu — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing stat.MEShow all

4 papers · 1 filter

stat.ME2026

Semiparametric Receiver Operating Characteristic Analysis in the Presence of an Imperfect Reference Standard via a Box-Cox Density Ratio Model

Yi Chang, Siyan Liu, Qinglong Tian +1

Receiver operating characteristic (ROC) analysis is commonly used to evaluate the diagnostic accuracy of continuous biomarkers. In practice, the true disease status may be unavaila…

stat.ME2026

Semiparametric Joint Inference for Sensitivity and Specificity at the Youden-Optimal Cut-Off

Siyan Liu, Qinglong Tian, Chunlin Wang +1

Sensitivity and specificity evaluated at an optimal diagnostic cut-off are fundamental measures of classification accuracy when continuous biomarkers are used for disease diagnosis…

stat.ME2025

Semiparametric Learning from Open-Set Label Shift Data

Siyan Liu, Yukun Liu, Qinglong Tian +2

We study the open-set label shift problem, where the test data may include a novel class absent from training. This setting is challenging because both the class proportions and th…

stat.ME2024

Positive and Unlabeled Data: Model, Estimation, Inference, and Classification

Siyan Liu, Chi-Kuang Yeh, Xin Zhang +2

This study introduces a new approach to addressing positive and unlabeled (PU) data through the double exponential tilting model (DETM). Traditional methods often fall short becaus…

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