This book introduces a new approach, denoted RMM, for an empirical modeling of a response variation, relating to both systematic variation and random variation. In the book, the developer of RMM discusses the required properties of empirical modeling and evaluates how current approaches conform to these requirements. In addition, he explains the motivation for the development of the new methodology, introduces in detail the new approach and its estimation procedures, and shows how it may provide an excellent alternative to current approaches for empirical modeling (like Generalized Linear Modeling, GLM). The book also demonstrates that a myriad of current relational models, developed independently in various engineering and scientific disciplines, are in fact special cases of the RMM model, and so are many current statistical distributions, transformations and approximations.
Models of Systematic Variation in Engineering and the Sciences; General Approaches to Modeling Systematic Variation; General Models of Random Variation; Empirical Modeling; Axiomatic Derivation of the RMM Model; Estimation Procedures; The Error Distribution; Modeling Systematic Variation-Applications: Hardware Reliability Models, Software Reliability-growth Models,Chemical-Engineering Models, Modeling and Forecasting S-shaped Diffusion Processes; Modeling Random Variation-Applications: Distributional Approximations, General Control Charts for Attributes and for Variables, Inventory Analysis.