Multivariate Fitting and the Error Matrix in Global Analysis of Data
arXiv:hep-ph/0008191 · doi:10.1103/PhysRevD.65.014011
Abstract
When a large body of data from diverse experiments is analyzed using a theoretical model with many parameters, the standard error matrix method and the general tools for evaluating errors may become inadequate. We present an iterative method that significantly improves the reliability of the error matrix calculation. To obtain even better estimates of the uncertainties on predictions of physical observables, we also present a Lagrange multiplier method that explores the entire parameter space and avoids the linear approximations assumed in conventional error propagation calculations. These methods are illustrated by an example from the global analysis of parton distribution functions.
13 pages, 5 figures, Latex; minor clarifications, fortran program made available; Normalization of Hessian matrix changed to HEP standard
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- Uncertainties of predictions from parton distribution functions II: the Hessian method
- Uncertainties of Predictions from Parton Distribution Functions I: the Lagrange Multiplier Method
- A new global analysis of deep inelastic scattering data
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