7 citations · 25 across the 13 of their papers we have counts for
15 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…
Unsupervised optimal deep transfer learning for classification under general conditional shift
Junjun Lang, Yukun Liu
Classifiers trained solely on labeled source data may yield misleading results when applied to unlabeled target data drawn from a different distribution. Transfer learning can rect…
Efficient estimation of partially linear additive Cox models and variance estimation under shape restrictions
Junjun Lang, Yukun Liu, Jing Qin
Shape-restricted inferences have exhibited empirical success in various applications with survival data. However, certain works fall short in providing a rigorous theoretical justi…
Hypothesis test on a mixture forward-incubation-time epidemic model with application to COVID-19 outbreak
Chunlin Wang, Pengfei Li, Yukun Liu +2
The distribution of the incubation period of the novel coronavirus disease that emerged in 2019 (COVID-19) has crucial clinical implications for understanding this disease and devi…
Nearly optimal capture-recapture sampling and empirical likelihood weighting estimation for M-estimation with big data
Yan Fan, Yang Liu, Yukun Liu +1
Subsampling techniques can reduce the computational costs of processing big data. Practical subsampling plans typically involve initial uniform sampling and refined sampling. With…
Penalized empirical likelihood estimation and EM algorithms for closed-population capture-recapture models
Yang Liu, Pengfei Li, Yukun Liu
Capture-recapture experiments are widely used to estimate the abundance of a finite population. Based on capture-recapture data, the empirical likelihood (EL) method has been shown…
Tuning-parameter-free optimal propensity score matching approach for causal inference
Yukun Liu, Jing Qin
Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or…
A selective review on calibration information from similar studies based on parametric likelihood or empirical likelihood
Jing Qin, Yukun Liu, Pengfei Li
In multi-center clinical trials, due to various reasons, the individual-level data are strictly restricted to be assessed publicly. Instead, the summarized information is widely av…
Full-semiparametric-likelihood-based inference for non-ignorable missing data
Yukun Liu, Pengfei Li, Jing Qin
During the past few decades, missing-data problems have been studied extensively, with a focus on the ignorable missing case, where the missing probability depends only on observab…
Inference for case-control studies with incident and prevalent cases
Marlena Maziarz, Yukun Liu, Jing Qin +1
We propose and study a fully efficient method to estimate associations of an exposure with disease incidence when both, incident cases and prevalent cases, i.e. individuals who wer…