BibTeX

@article{2502.11072v2,
Author        = {Elena Bortolato and Laura Ventura},
Title         = {Box Confidence Depth: simulation-based inference with hyper-rectangles},
Eprint        = {2502.11072v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {This work presents a novel simulation-based approach for constructing
confidence regions in parametric models, which is particularly suited for
generative models and situations where limited data and conventional asymptotic
approximations fail to provide accurate results. The method leverages the
concept of data depth and depends on creating random hyper-rectangles, i.e.
boxes, in the sample space generated through simulations from the model,
varying the input parameters. A probabilistic acceptance rule allows to
retrieve a Depth-Confidence Distribution for the model parameters from which
point estimators as well as calibrated confidence sets can be read-off. The
method is designed to address cases where both the parameters and test
statistics are multivariate.},
Year          = {2025},
Month         = {Feb},
Url           = {http://arxiv.org/abs/2502.11072v2},
File          = {2502.11072v2.pdf}
}