7 papers
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…
Retrospective score tests versus prospective score tests for genetic association with case-control data
Yukun Liu, Pengfei Li, Lei Song +2
Since the seminal work by Prentice and Pyke (1979), the prospective logistic likelihood has become the standard method of analysis for retrospectively collected case-control data,…
Maximum likelihood abundance estimation from capture-recapture data when covariates are missing at random
Yang Liu, Yukun Liu, Pengfei Li +2
In capture-recapture experiments, individual covariates may be subject to missing, especially when the number of times of being captured is small. When the covariate information is…
Hypothesis testing for quantitative trait locus effects in both location and scale in genetic backcross studies
Guanfu Liu, Pengfei Li, Yukun Liu +1
Testing the existence of a quantitative trait locus (QTL) effect is an important task in QTL mapping studies. Most studies concentrate on the case where the phenotype distributions…
Empirical likelihood meta analysis with publication bias correction under Copas-like selection model
Mengke Li, Yukun Liu, Pengfei Li +1
Meta analysis is commonly-used to synthesize multiple results from individual studies. However, its validation is usually threatened by publication bias and between-study heterogen…
Revamping Conformal Selection With Optimal Power: A Neyman--Pearson Perspective
Jing Qin, Yukun Liu, Moming Li +1
This paper introduces a novel conformal selection procedure, inspired by the Neyman--Pearson paradigm, to maximize the power of selecting qualified units while maintaining false di…