Estimation of Goodness-of-Fit in Multidimensional Analysis Using Distance to Nearest Neighbor

2003/06/24 by Ilya Narsky, Narsky, Ilya
Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Statistics and Probability (physics.data-an) #physics.data-an

paper · pdf · doi:10.48550/arxiv.physics/0306171

15 pages, 8 figures, 1 table

arxiv created 2003/06/24 · arxiv updated 2009/12/01

Abstract

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.

Related

• Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis
• Machine learning and multivariate goodness of fit
• Computing the asymptotic power of a Euclidean-distance test for goodness-of-fit
• Goodness-of-Fit Tests for Large Datasets
• A goodness-of-fit test based on a recursive product of spacings