
Response Modeling Methodology: Empirical Modeling For Engineering And Science
Haim Shore(Author)
World Scientific Publishing Co Pte Ltd
Will be published approx. on 26. April 2005
Book
Hardback
460 pages
978-981-256-102-2 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Product notice
Laminated cover
Dimensions
Height: 236 mm
Width: 157 mm
Thickness: 30 mm
Weight
780 gr
ISBN-13
978-981-256-102-2 (9789812561022)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Content
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.