Statistical Models with Uncertain Error Parameters

TL;DR

This paper introduces a statistical model that accounts for uncertainty in systematic error estimates in particle physics, leading to more robust confidence intervals that adapt to data quality and outliers.

Contribution

It presents a novel approach modeling systematic error variances as gamma-distributed variables, enhancing the robustness of statistical inferences in particle physics analyses.

Findings

Confidence intervals increase with decreasing goodness-of-fit.

Averages become less sensitive to outliers.

The model provides useful properties for particle physics measurements.

Abstract

In a statistical analysis in Particle Physics, nuisance parameters can be introduced to take into account various types of systematic uncertainties. The best estimate of such a parameter is often modeled as a Gaussian distributed variable with a given standard deviation (the corresponding "systematic error"). Although the assigned systematic errors are usually treated as constants, in general they are themselves uncertain. A type of model is presented where the uncertainty in the assigned systematic errors is taken into account. Estimates of the systematic variances are modeled as gamma distributed random variables. The resulting confidence intervals show interesting and useful properties. For example, when averaging measurements to estimate their mean, the size of the confidence interval increases for decreasing goodness-of-fit, and averages have reduced sensitivity to outliers. The basic properties of the model are presented and several examples relevant for Particle Physics are explored.