  Several authors, including the American Statistician (ASA), have noted the
challenges facing statisticians when attacking large, complex, unstructured
problems, as opposed to well-defined textbook problems. Clearly, the standard
paradigm of selecting the one "correct" statistical method for such problems is
not sufficient; a new paradigm is needed. Statistical engineering has been
proposed as a discipline that can provide a viable paradigm to attack such
problems, used in conjunction with sound statistical science. Of course, in
order to develop as a true discipline, statistical engineering needs a
well-developed theory, not just a formal definition and successful case
studies. This article documents and disseminates the current state of the
underlying theory of statistical engineering. Our purpose is to provide a
vehicle for applied statisticians to further enhance the practice of
statistics, and for academics so interested to continue development of the
underlying theory of statistical engineering.
