  The goal of regression analysis is to predict the value of a numeric outcome
variable y given a vector of joint values of other (predictor) variables x.
Usually a particular x-vector does not specify a repeatable value for y, but
rather a probability distribution of possible y--values, p(y|x). This
distribution has a location, scale and shape, all of which can depend on x, and
are needed to infer likely values for y given x. Regression methods usually
assume that training data y-values are perfect numeric realizations from some
well behaived p(y|x). Often actual training data y-values are discrete,
truncated and/or arbitrary censored. Regression procedures based on an optimal
transformation strategy are presented for estimating location, scale and shape
of p(y|x) as general functions of x, in the possible presence of such imperfect
training data. In addition, validation diagnostics are presented to ascertain
the quality of the solutions.
