Error estimation and reduction with cross correlations

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.Comment: 16 pages, RevTEX4, 4 figures, 6 tables, published versio

Efficiency of propensity score adjustment and calibration on the estimation from non-probabilistic online surveys

Peer Reviewe

Computational Strategies in Lattice QCD

Lectures given at the Summer School on "Modern perspectives in lattice QCD", Les Houches, August 3-28, 2009Comment: Latex source, 72 pages, 23 figures; v2: misprints corrected, minor text change