Applied Predictive Modeling
Springer-Verlag New York Inc.
978-1-4939-7936-3 (ISBN)
This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package.
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms.
General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.
Erscheinungsdatum | 20.12.2018 |
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Zusatzinfo | 296 Illustrations, color; 14 Illustrations, black and white; XIII, 600 p. 310 illus., 296 illus. in color. |
Verlagsort | New York |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra |
Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie | |
Schlagworte | Model • Non-linear • predictive models • R • regression models • regression trees |
ISBN-10 | 1-4939-7936-1 / 1493979361 |
ISBN-13 | 978-1-4939-7936-3 / 9781493979363 |
Zustand | Neuware |
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