BibTeX

@article{2508.02404v1,
Author        = {Lorenzo Tomaselli and Valérie Ventura and Larry Wasserman},
Title         = {Robust Simulation Based Inference},
Eprint        = {2508.02404v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {Simulation-Based Inference (SBI) is an approach to statistical inference
where simulations from an assumed model are used to construct estimators and
confidence sets. SBI is often used when the likelihood is intractable and to
construct confidence sets that do not rely on asymptotic methods or regularity
conditions. Traditional SBI methods assume that the model is correct, but, as
always, this can lead to invalid inference when the model is misspecified. This
paper introduces robust methods that allow for valid frequentist inference in
the presence of model misspecification. We propose a framework where the target
of inference is a projection parameter that minimizes a discrepancy between the
true distribution and the assumed model. The method guarantees valid inference,
even when the model is incorrectly specified and even if the standard
regularity conditions fail. Alternatively, we introduce model expansion through
exponential tilting as another way to account for model misspecification. We
also develop an SBI based goodness-of-fit test to detect model
misspecification. Finally, we propose two ideas that are useful in the SBI
framework beyond robust inference: an SBI based method to obtain closed form
approximations of intractable models and an active learning approach to more
efficiently sample the parameter space.},
Year          = {2025},
Month         = {Aug},
Url           = {http://arxiv.org/abs/2508.02404v1},
File          = {2508.02404v1.pdf}
}