Regression Modeling Strategies

With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis

Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Science & Nature, Mathematics, Statistics
Cover of the book Regression Modeling Strategies by Frank E. Harrell, Jr., Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Frank E. Harrell, Jr. ISBN: 9783319194257
Publisher: Springer International Publishing Publication: August 14, 2015
Imprint: Springer Language: English
Author: Frank E. Harrell, Jr.
ISBN: 9783319194257
Publisher: Springer International Publishing
Publication: August 14, 2015
Imprint: Springer
Language: English

This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. 

Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.

As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques. 

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. 

Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.

As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques. 

More books from Springer International Publishing

Cover of the book Computational Electrostatics for Biological Applications by Frank E. Harrell, Jr.
Cover of the book Central European Functional Programming School by Frank E. Harrell, Jr.
Cover of the book Robust Subspace Estimation Using Low-Rank Optimization by Frank E. Harrell, Jr.
Cover of the book A Treatise of Indian and Tropical Soils by Frank E. Harrell, Jr.
Cover of the book Contradictions, from Consistency to Inconsistency by Frank E. Harrell, Jr.
Cover of the book Primer on Client-Side Web Security by Frank E. Harrell, Jr.
Cover of the book Cloud Computing, Security, Privacy in New Computing Environments by Frank E. Harrell, Jr.
Cover of the book Arts, Research, Innovation and Society by Frank E. Harrell, Jr.
Cover of the book The Discourse of ADHD by Frank E. Harrell, Jr.
Cover of the book Dynamic Behavior of Materials, Volume 1 by Frank E. Harrell, Jr.
Cover of the book Cities and Mega-Cities by Frank E. Harrell, Jr.
Cover of the book Sentiment Analysis and Ontology Engineering by Frank E. Harrell, Jr.
Cover of the book Intelligent Computer Mathematics by Frank E. Harrell, Jr.
Cover of the book Road Vehicle Automation by Frank E. Harrell, Jr.
Cover of the book Enterprise Information Systems by Frank E. Harrell, Jr.
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy