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Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Springer Series in Statistics) 2nd Edition, Kindle Edition

by Frank E. Harrell Jr. (Author) Format: Kindle Edition
4.6 4.6 out of 5 stars 62 ratings
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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 modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty and its effects on inference, to achieve "safe data mining." It also presents many graphical methods for communicating complex regression models to non-statisticians.
Regression Modeling Strategies presents full-scale case studies of non-trivial datasets 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 generalized 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 Ph.D. 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 modeling techniques. Examples used in the text mostly come from biomedical research, but the methods are applicable anywhere predictive models ("analytics") are useful, including economics, epidemiology, sociology, psychology, engineering and marketing.
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  1. ISBN-13
    978-3319194240
  2. Edition
    2
  3. Sticky notes
    Not enabled
  4. Publisher
    Springer
  5. Publication date
    14 August 2015
  6. Part of series
    Springer Series in Statistics
  7. Language
    English
  8. File size
    13893 KB
  9. See all details
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Product description

Review

“The aim and scope of this edition to provide graduate students and professional and early career researchers with insights, understandings and working knowledge of regression modelling. … . The book is sequentially organized and well structured and many chapters are self-contained. It includes many useful topics and techniques for graduate .students and researchers alike. This book can be used as a textbook and equally as a reference book.” (Technometrics, Vol. 58 (2), February, 2016)

From the Back Cover

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 modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty, and its effects on inference, to achieve "safe data mining." It also presents many graphical methods for communicating complex regression models to non-statisticians.

Regression Modeling Strategies presents full-scale case studies of non-trivial datasets 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 generalized least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models, and the Cox semiparametric 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 Ph.D. 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 modeling techniques. Examples used in the text mostly come from biomedical research, but the methods are applicable anywhere predictive models ("analytics") are useful, including economics, epidemiology, sociology, psychology, engineering, and marketing.

Product details

  • ASIN ‏ : ‎ B0140XQAXI
  • Publisher ‏ : ‎ Springer; 2 edition (14 August 2015)
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 13893 KB
  • Text-to-Speech ‏ : ‎ Not enabled
  • Enhanced typesetting ‏ : ‎ Not Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Sticky notes ‏ : ‎ Not enabled
  • Print length ‏ : ‎ 607 pages
  • Customer Reviews:
    4.6 4.6 out of 5 stars 62 ratings

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Frank E. Harrell Jr.

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Anonym
5.0 out of 5 stars Best book on regression modeling
Reviewed in Germany on 28 November 2022
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Frank Harrell's way of explaining methods along with R code is very useful, even for beginners.
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Flavio M. M. Barros
5.0 out of 5 stars Bibliografia OBRIGATÓRIA para estatísticos e cientistas de dados
Reviewed in Brazil on 27 November 2019
Verified Purchase
Eu fiz uma graduação em estatística e quando eu fiz a disciplina de regressão, ou mesmo as disciplinas de projetos finais, ter lido esse livro teria feito toda a diferença. Esse livro aborda diversos fundamentos da modelagem que não são ensinados tradicionalmente em um curso de regressão. Alguns conhecimentos que impactaram bastante minha forma de trabalhar com modelagem foram:

1 - como a discretização tem um impacto negativo na modelagem;
2 - a importância de utilizar bootstrap em modelagem, avaliação de modelos e etc;
3 - como não jogar informações fora com técnicas de modelagem ruins;
4 - a importância de criar modelos interpretáveis ao invés de caixas-preta;

e muito e muito mais. Esse deveria ser um livro de cabeceira para todo estatístico aplicado ou cientista de dados.

Tem algum ponto ruim do livro? O autor é um bioestatístico e a maioria do exemplos são dessa área e com esse viés. Para mim não é de fato um problema mas pode ser algo relevante para alguns.
7 people found this helpful
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Bruno
4.0 out of 5 stars Un clásico del análisis de regresión
Reviewed in Spain on 30 June 2019
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Un clásico en el análisis de la regresión. El autor desarrolla aquí su filosofía metodológica (a favor de la “regresión-regresión” en contraste con otras metodologías de “clasificación” y “black box” tan en boga en machine learning). Es muy pedagógico y apto para gente con distinta formación matemática
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Amazon Customer
5.0 out of 5 stars Very Informative
Reviewed in the United Kingdom on 6 May 2017
Verified Purchase
I purchased this book both for the study of data science and addressing a specific regression problem at work. Unlike more introductory textbooks, this book explains more practicalities as to which tests or techniques are better suited for the situation. It helps to know better what to do when the data is not ideal.
One person found this helpful
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R. Angels
5.0 out of 5 stars Very nice and useful
Reviewed in Canada on 6 June 2013
Verified Purchase
Very nice, extremely useful knowledge for people who want to understand regression modelling, easy to understand and has good examples. Hopefully there will be a newer version coming up.
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