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  On non-parametric tests for discovery and limit setting in one and multiple dimensions

Shtembari, L. (2023). On non-parametric tests for discovery and limit setting in one and multiple dimensions. PhD Thesis, TUM, München.

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https://mediatum.ub.tum.de/?id=1707763 (Any fulltext)
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 Creators:
Shtembari, Lolian1, Author
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1Max Planck Institute for Physics, Max Planck Society and Cooperation Partners, ou_2253650              

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Free keywords: Statistical Methods
 Abstract: Spacings between ordered data are used to develop unbinned non-parametric Goodness of Fit (GoF) tests to detect unknown signals against known backgrounds or set limits on proposed signals contaminated by unknown backgrounds. I also extend GoF tests to multivariate samples, using a multivariate probability integral transformation, carried out either analytically or numerically using a Normalizing Flow, ultimately reducing the problem to a single or multiple univariate uniformity tests.

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 Dates: 2023-07-28
 Publication Status: Accepted / In Press
 Pages: -
 Publishing info: München : TUM
 Table of Contents: -
 Rev. Type: -
 Degree: PhD

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