Peer-reviewedLicence not statedThe Annals of Applied Statistics
Scale reliant inference
5 affiliations
- 1College of Information Sciences and Technology, Pennsylvania State University
- 2Program in Bioinformatics and Genomics, Pennsylvania State University
- 3Department of Molecular Genetics and Microbiology, Duke University
- 4Department of Statistics, Pennsylvania State University
- 5Departments of Statistical Science, Mathematics, Computer Science, Biostatistics & Bioinformatics, Duke University
Related
- Finite-Sample Distribution Theory and Efficient Large-Scale Inference for Online Quantile Regression
- Large-scale empirical tuning and comparison of default optimizers for variational inference
- MCMC-Free Uncertainty Quantification for Deep Generative Inference for Spatially Varying Coefficient Models at Scale
- Distributed Proximal Stein Variational Gradient Descent Algorithm for Large-scale Bayesian Inference in Traveltime Tomography
- Semiparametrically efficient semi-supervised statistical inference
BibTeX
@article{nixon2026scale,
title = {{Scale reliant inference}},
author = {Michelle Pistner Nixon and Kyle C. McGovern and Maxwell A. Konnaris and Jeffrey Letourneau and Lawrence A. David and Nicole A. Lazar and Sayan Mukherjee and Justin D. Silverman},
journal = {The Annals of Applied Statistics},
year = {2026},
month = sep,
doi = {10.1214/26-aoas2230},
url = {http://arxiv.org/abs/2201.03616},
}
RIS
TY - JOUR
TI - Scale reliant inference
AU - Michelle Pistner Nixon
AU - Kyle C. McGovern
AU - Maxwell A. Konnaris
AU - Jeffrey Letourneau
AU - Lawrence A. David
AU - Nicole A. Lazar
AU - Sayan Mukherjee
AU - Justin D. Silverman
PY - 2026
DA - 2026/09/01
JO - The Annals of Applied Statistics
DO - 10.1214/26-aoas2230
UR - http://arxiv.org/abs/2201.03616
SN - 1932-6157
SN - 1941-7330
ER -