论文标题
适合相关和自动相关的数据
On fits to correlated and auto-correlated data
论文作者
论文摘要
通常从拟合中提取粒子物理学，特别是晶格QCD计算中的可观察物。标准$χ^2 $测试需要可靠地确定协方差矩阵及其与相关和自动相关的数据倒数，这是一项艰巨的任务，这通常导致近距离估计。这些激励对$χ^2 $的定义（例如不相关拟合）的定义进行了修改。我们展示了如何通过其p值衡量的拟合优度对于一系列此类拟合而进行稳健估计。
Observables in particle physics and specifically in lattice QCD calculations are often extracted from fits. Standard $χ^2$ tests require a reliable determination of the covariance matrix and its inverse from correlated and auto-correlated data, a challenging task often leading to close-to-singular estimates. These motivate modifications of the definition of $χ^2$ such as uncorrelated fits. We show how the goodness-of-fit measured by their p-value can still be estimated robustly for a broad class of such fits.
