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Learning Optimal Test Statistics in the Presence of Nuisance Parameters

L Heinrich - arXiv preprint arXiv:2203.13079, 2022 - arxiv.org
Statistics paper stat.ME Suggest

… Related Work Likelihood-free and simulation-based inference using machine-learning methods are growing field of research, where inference approaches for both Bayesian and …

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BibTeX

@article{2203.13079v1,
Author        = {Lukas Heinrich},
Title         = {Learning Optimal Test Statistics in the Presence of Nuisance Parameters},
Eprint        = {2203.13079v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {The design of optimal test statistics is a key task in frequentist statistics
and for a number of scenarios optimal test statistics such as the
profile-likelihood ratio are known. By turning this argument around we can find
the profile likelihood ratio even in likelihood-free cases, where only samples
from a simulator are available, by optimizing a test statistic within those
scenarios. We propose a likelihood-free training algorithm that produces test
statistics that are equivalent to the profile likelihood ratios in cases where
the latter is known to be optimal.},
Year          = {2022},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2203.13079v1},
File          = {2203.13079v1.pdf}
}

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