Papers › Inference in generalized bilinear models
Inference in generalized bilinear models
Jeffrey W. Miller, Scott L. Carter
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Latent factor models are widely used to discover and adjust for hidden variation in modern applications. However, most methods do not fully account for uncertainty in the latent factors, which can lead to miscalibrated inferences such as overconfident p-values. In this article, we develop a fast and accurate method of uncertainty quantification in generalized bilinear models, which are a flexible extension of generalized linear models to include latent factors as well as row covariates, column covariates, and interactions. In particular, we introduce delta propagation, a general technique for propagating uncertainty among model components using the delta method. Further, we provide a rapidly converging algorithm for maximum a posteriori GBM estimation that extends earlier methods by estimating row and column dispersions. In simulation studies, we find that our method provides approximately correct frequentist coverage of most parameters of interest. We demonstrate on RNA-seq gene expression analysis and copy ratio estimation in cancer genomics.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections