activity
20162022
most citedItem Response Theory -- A Statistical Framework for Educational and Psychological Measurement

17 citations · 35 across the 8 of their papers we have counts for

collaborators

10 papers

stat.ME202117 cited

Item Response Theory -- A Statistical Framework for Educational and Psychological Measurement

Yunxiao Chen, Xiaoou Li, Jingchen Liu +1

Item response theory (IRT) has become one of the most popular statistical models for psychometrics, a field of study concerned with the theory and techniques of psychological measu…

stat.ME2020

Determining the Number of Factors in High-dimensional Generalized Latent Factor Models

Yunxiao Chen, Xiaoou Li

As a generalization of the classical linear factor model, generalized latent factor models are useful for analyzing multivariate data of different types, including binary choices a…

stat.ME2018

Spherical Regression under Mismatch Corruption with Application to Automated Knowledge Translation

Xu Shi, Xiaoou Li, Tianxi Cai

Motivated by a series of applications in data integration, language translation, bioinformatics, and computer vision, we consider spherical regression with two sets of unit-length…

stat.ME20171 cited

Asymptotically Optimal Sequential Design for Rank Aggregation

Xi Chen, Yunxiao Chen, Xiaoou Li

A sequential design problem for rank aggregation is commonly encountered in psychology, politics, marketing, sports, etc. In this problem, a decision maker is responsible for ranki…

math.PR2017

Uniformly Efficient Simulation for Extremes of Gaussian Random Fields

Xiaoou Li, Gongjun Xu

This paper considers the problem of simultaneously estimating rare-event probabilities for a class of Gaussian random fields. A conventional rare-event simulation method is usually…

stat.ME20174 cited

Optimal Stopping and Worker Selection in Crowdsourcing: an Adaptive Sequential Probability Ratio Test Framework

Xiaoou Li, Yunxiao Chen, Xi Chen +2

In this paper, we aim at solving a class of multiple testing problems under the Bayesian sequential decision framework. Our motivating application comes from binary labeling tasks…