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20242026
most citedNew directions in algebraic statistics: Three challenges from 2023

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

stat.ME2026

Covariate-assisted Grade of Membership Models via Shared Latent Geometry

Zhiyu Xu, Yuqi Gu

The grade of membership model is a flexible latent variable model for analyzing multivariate categorical data through individual-level mixed membership scores. In many modern appli…

stat.ME2026

Valid and Efficient Two-Stage Latent Subgroup Analysis with Observational Data

Yuanhui Luo, Xinzhou Guo, Yuqi Gu

Subgroup analysis evaluates treatment effects across multiple sub-populations. When subgroups are defined by latent memberships inferred from imperfect measurements, the analysis t…

stat.ME2025

Spectral Clustering with Likelihood Refinement for High-dimensional Latent Class Recovery

Zhongyuan Lyu, Yuqi Gu

Latent class models are widely used for identifying unobserved subgroups from multivariate categorical data in social sciences, with binary data as a particularly popular example.…

math.ST2025

Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models

Yuqi Gu

We use a tensor unfolding technique to prove a new identifiability result for discrete bipartite graphical models, which have a bipartite graph between an observed and a latent lay…

math.ST2024★ 1 cited

New directions in algebraic statistics: Three challenges from 2023

Yulia Alexandr, Miles Bakenhus, Mark Curiel +11

In the last quarter of a century, algebraic statistics has established itself as an expanding field which uses multilinear algebra, commutative algebra, computational algebra, geom…