Göttingen 2025 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

T: Fachverband Teilchenphysik

T 11: Data, AI, Computing, Electronics I (Statistical Methods, Applications)

T 11.4: Vortrag

Montag, 31. März 2025, 17:30–17:45, VG 2.101

Hypothesis tests and model parameter estimation on data sets with missing correlation information — •Lukas Koch — JGU Mainz

Ideally, all analyses of normally distributed data should include the full covariance information between all data points. In practice, the full covariance matrix between all data points is not always available. Either because a result was published without a covariance matrix, or because one tries to combine multiple results from separate publications. For simple hypothesis tests, it is possible to define robust test statistics that will behave conservatively in the presence on unknown correlations. For model parameter fits, one can inflate the variance by factor to ensure that things remain conservative at least up to a chosen confidence level. In this talk I will describe a class of robust test statistics for simple hypothesis tests, as well as an algorithm to determine the necessary inflation factor for model parameter fits.

Keywords: Fit; Hypothesis test; Statistical method; Covariance; Correlation