  1. Parameter inference from distorted measurements is discussed. 2. Smeared
measurements are unfolded without explicit regularization. The corresponding
results are unbiased and permit to fit parameters and to apply quantitative
goodness-of-fit tests. 3. Common unfolding methods (iterative EM with early
stopping, truncated SVD, ML fits with curvature, entropy and norm penalties)
are tested and compared to each other with the regularization parameter
adjusted to minimize the integrated square error (ISE) in all cases. Apart from
histogram representations, spline approximations are considered. All
simulations indicate that the EM method leads to smaller ISEs than the
competing approaches. Especially promising is the EM unfolding to spline
approximations.
  The studies are based on different distributions, event numbers, resolutions
and enough independent simulations to obtain conclusive results. It is proposed
to unfold data with the EM method to b-spline approximations and to supplement
the results with histograms and error matrices obtained by unfolding into
relative wide bins without explicit regularization.
