K-2 rotated goodness-of-fit for multivariate data

Consider a set of multivariate distributions, \(F_1,\dots,F_M\), aiming to explain the same phenomenon. For instance, each \(F_m\) may correspond to a different candidate background model for calibration data, or to one of many possible signal models we aim to validate on experimental data. In this...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-02
Main Author: Algeri, Sara
Format: Article
Language:English
Subjects:
Computing time
Goodness of fit
Multivariate analysis
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Consider a set of multivariate distributions, \(F_1,\dots,F_M\), aiming to explain the same phenomenon. For instance, each \(F_m\) may correspond to a different candidate background model for calibration data, or to one of many possible signal models we aim to validate on experimental data. In this article, we show that tests for a wide class of apparently different models \(F_{m}\) can be mapped into a single test for a reference distribution \(Q\). As a result, valid inference for each \(F_m\) can be obtained by simulating \underline{only} the distribution of the test statistic under \(Q\). Furthermore, \(Q\) can be chosen conveniently simple to substantially reduce the computational time.
ISSN:2331-8422
DOI:10.48550/arxiv.2202.02597