  Error propagation formulae are derived for the expectation-maximization
iterative unfolding algorithm regularized by a smoothing step. The effective
number of parameters in the fit to the observed data is defined for unfolding
procedures. Based upon this definition, the Akaike information criterion is
proposed as a principle for choosing the smoothing parameters in an automatic,
data-dependent manner. The performance and the frequentist coverage of the
resulting method are investigated using simulated samples. A number of issues
of general relevance to all unfolding techniques are discussed, including
irreducible bias, uncertainty increase due to a data-dependent choice of
regularization strength, and presentation of results.
