Distribution Fitting with Response Modeling Methodology (RMM) — Some Recent Results

Abstract

SYNOPTIC ABSTRACTResponse Modeling Methodology (RMM) is a new approach for empirical modeling of systematic variation and of random variation. Applied to various fields of science, engineering and operations management, RMM has been shown to deliver good modeling capabilities while preserving desirable “uniformity of practice” across widely divergent disciplines. In this paper, RMM is briefly outlined, and its basic philosophy, relative to other approaches, is discussed. A detailed numerical example demonstrates application of RMM for distribution fitting and compares the results to fitting by generalized lambda distribution. Initial results are described from an ongoing research that statistically compares goodness-of-fit obtained from fitting several families of distributions to a sample of commonly applied distributions. The results suggest that it is possible to rank widely used families of distributions in terms of their capability to serve as general platforms for distribution fitting.