Applied Predictive Modeling
By: by Max Kuhn, Kjell Johnson
Contributors: Johnson, Kjell; SpringerLink (Online service)
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 600
ISBN-13: 9781461468493
Subjects: Mathematical statistics; Statistics; Statistics, general; Statistics for Life Sciences, Medicine, Health Sciences; Statistics and Computing/Statistics Programs
Description: 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.
Table of contents: title: General Strategies; title: Regression Models; title: Classification Models; title: Other Considerations; title: Appendix; title: References; title: Indices.