| Response Modeling Methodology: Empirical Modeling for Engineering and Science Contributor(s): Shore, Haim (Author) |
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ISBN: 9812561021 ISBN-13: 9789812561022 Publisher: World Scientific Publishing Company OUR PRICE: $161.50 Product Type: Hardcover Published: April 2005 Annotation: - Demonstrates how the new approach (RMM) differs from current approaches in that both the structure of the model and its parameters are determined via data-driven procedures - Demonstrates that a single comprehensive methodology may provide a good platform for empirical modeling of both systematic variation (relational modeling) and random variation (variation that is captured by a statistical distribution with stable parameters) - Provides handy procedures to apply to the new methodology, accompanied by detailed numerical examples for the implementation of these procedures |
| Additional Information |
| BISAC Categories: - Business & Economics | Operations Research - Business & Economics | Management Science - Mathematics | Probability & Statistics - General |
| Dewey: 620.004 |
| Series: Quality, Reliability and Engineering Statistics |
| Physical Information: 1.17" H x 6.18" W x 9.3" (1.72 lbs) 460 pages |
| Descriptions, Reviews, Etc. |
| Publisher 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. |