Error estimation and reduction with cross correlations
Statistical Mechanics
2014-11-20 v2 High Energy Physics - Lattice
Abstract
Besides the well-known effect of autocorrelations in time series of Monte Carlo simulation data resulting from the underlying Markov process, using the same data pool for computing various estimates entails additional cross correlations. This effect, if not properly taken into account, leads to systematically wrong error estimates for combined quantities. Using a straightforward recipe of data analysis employing the jackknife or similar resampling techniques, such problems can be avoided. In addition, a covariance analysis allows for the formulation of optimal estimators with often significantly reduced variance as compared to more conventional averages.
Cite
@article{arxiv.1002.4517,
title = {Error estimation and reduction with cross correlations},
author = {Martin Weigel and Wolfhard Janke},
journal= {arXiv preprint arXiv:1002.4517},
year = {2014}
}
Comments
16 pages, RevTEX4, 4 figures, 6 tables, published version