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While considerable advance has been made to account for statisticaluncertainties in astronomical analyses, systematic instrumental uncertaintieshave been generally ignored. This can be crucial to a proper interpretation ofanalysis results because instrumental calibration uncertainty is a form ofsystematic uncertainty. Ignoring it can underestimate error bars and introducebias into the fitted values of model parameters. Accounting for suchuncertainties currently requires extensive case-specific simulations if usingexisting analysis packages. Here we present general statistical methods thatincorporate calibration uncertainties into spectral analysis of high-energydata. We first present a method based on multiple imputation that can beapplied with any fitting method, but is necessarily approximate. We thendescribe a more exact Bayesian approach that works in conjunction with a Markovchain Monte Carlo based fitting. We explore methods for improving computationalefficiency, and in particular detail a method of summarizing calibrationuncertainties with a principal component analysis of samples of plausiblecalibration files. This method is implemented using recently codified Chandraeffective area uncertainties for low-resolution spectral analysis and isverified using both simulated and actual Chandra data. Our procedure forincorporating effective area uncertainty is easily generalized to other typesof calibration uncertainties.

Accounting for Calibration Uncertainties in X-ray Analysis: Effective Areas in Spectral Fitting