Identify the source of spikes: factor or mixture?
Authors
Paper Information
-
Journal:
Biometrika -
DOI:
10.1093/biomet/asag044 -
Published:
June 29, 2026 -
Added to Tracker:
Jun 30, 2026
Abstract
Summary We consider the problem of identifying the pattern of latent variables in high-dimensional linear latent variable models, which can also be interpreted as determining the source of spiked singular values in the data matrix. Specifically, we test whether the latent variables are continuous or categorical, a distinction which is crucial for data interpretation but challenging in the high-dimensional regime. To address this inference problem, we analyze the asymptotic behavior of empirical measures associated with singular vectors corresponding to large spiked singular values. Leveraging these insights,we propose novel test statistics based on the eigenvector quantile differences and establish their theoretical performance under the null hypothesis. Simulation studies and real data analyses demonstrate the effectiveness and practical utility of our method.
Author Details
Guangming Pan
AuthorZeqin Lin
AuthorYiming Liu
AuthorChi Yao
AuthorJia Zhou
AuthorCitation Information
APA Format
Guangming Pan
,
Zeqin Lin
,
Yiming Liu
,
Chi Yao
&
Jia Zhou
(2026)
.
Identify the source of spikes: factor or mixture?.
Biometrika
, 10.1093/biomet/asag044.
BibTeX Format
@article{paper1357,
title = { Identify the source of spikes: factor or mixture? },
author = {
Guangming Pan
and Zeqin Lin
and Yiming Liu
and Chi Yao
and Jia Zhou
},
journal = { Biometrika },
year = { 2026 },
doi = { 10.1093/biomet/asag044 },
url = { https://doi.org/10.1093/biomet/asag044 }
}