"Response Modeling Methodology: Empirical Modeling for Engineering and Science" by Haim Shore (Repost)

This book introduces a new approach, denoted Response Modeling Methodology (RMM), for an empirical modeling of a response variation, relating to both systematic variation and random variation. 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.

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).

Contents
Preface

1 Introduction
2 Relational Models in Engineering and the Sciences (Monotone Convex/Concave Relationships)
3 Shared Features and 'The Ladder"
4 Approaches to Model Systematic Variation
5 Approaches to Model Random Variation
6 The Requirements and Evaluation of Compliance
7 The RMM Model
8 Estimating the Relational Model
9 The RMM Error Distribution
10 Fitting Procedures (for the Error Distribution)
11 Estimating the Error Distribution
12 Special Cases of the RMM Model
13 Evaluating RMM for Compliance
14 Comparative Solutions for Relational Models
15 Reliability Engineering (with Censoring)
16 Software Reliability-Growth Models
17 Modeling a Chemo-Response
18 Forecasting S-Shaped Diffusion Processes
19 RMM Distributional Approximations
20 Inverse Normalizing Transformations
21 Piece-Wise Linear Approximations
22 General Control Charts
23 Inventory Analysis
Review Questions
Author Index
Subject Index