利用最近邻距离估计多维分析的拟合优度
Estimation of Goodness-of-Fit in Multidimensional Analysis Using Distance to Nearest Neighbor
摘要：
提出了一种计算粒子物理实验中多维拟合优度的新方法。该方法为观察到的数据点找到最小和最大的最近邻聚类。聚类大小用于估计拟合优度，聚类位置提供有关数据建模可能存在的问题的线索。新方法的性能进行了比较的似然方法和Kolmogorov-Smirnov测试使用玩具蒙特卡洛研究。在BaBar的B->Kll分析中，新方法被应用于估计拟合优度。
英文摘要：
A new method for calculation of goodness of multidimensional fits in particle physics experiments is proposed. This method finds the smallest and largest clusters of nearest neighbors for observed data points. The cluster size is used to estimate the goodness-of-fit and the cluster location provides clues about possible problems with data modeling. The performance of the new method is compared to that of the likelihood method and Kolmogorov-Smirnov test using toy Monte Carlo studies. The new method is applied to estimate the goodness-of-fit in a B->Kll analysis at BaBar.
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