PreprintarXiv licencearXiv
Efficient computation of mixture confidence sequences in generalized linear models
Abstract
Classical confidence intervals, when repeatedly obtained on accumulating data at different sample sizes, produce contradictory inferences with high probability. We propose a simple and efficient strategy for computing, instead, mixture confidence sequences for regression coefficients in generalized linear models under this sequential framework. Simulations demonstrate the computational convenience of our approach and the importance of drawing inferential conclusions based on these anytime-valid tools when observations become available in batches over time. The usefulness of the proposed procedure is also shown in the analysis of streaming data from the American National Automotive Sampling System.
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BibTeX
@misc{caterina2026efficient,
title = {{Efficient computation of mixture confidence sequences in generalized linear models}},
author = {Claudia Di Caterina and Luigi Pace and Alessandra Salvan and Nicola Sartori},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.20496},
eprint = {2609.20496},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.20496},
}
RIS
TY - UNPB
TI - Efficient computation of mixture confidence sequences in generalized linear models
AU - Claudia Di Caterina
AU - Luigi Pace
AU - Alessandra Salvan
AU - Nicola Sartori
PY - 2026
DA - 2026/09/17
PB - arXiv
DO - 10.48550/arxiv.2609.20496
UR - https://arxiv.org/abs/2609.20496
AB - Classical confidence intervals, when repeatedly obtained on accumulating data at different sample sizes, produce contradictory inferences with high probability. We propose a simple and efficient strategy for computing, instead, mixture confidence sequences for regression coefficients in generalized linear models under this sequential framework. Simulations demonstrate the computational convenience of our approach and the importance of drawing inferential conclusions based on these anytime-valid tools when observations become available in batches over time. The usefulness of the proposed procedure is also shown in the analysis of streaming data from the American National Automotive Sampling System.
ER -