Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Springer Series in Statistics) By Frank E. Harrell Jr.

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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.

At this time of writing, The Audiobook Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Springer Series in Statistics) has garnered 8 customer reviews with rating of 5 out of 5 stars. Not a bad score at all as if you round it off, it’s actually a perfect TEN already. From the looks of that rating, we can say the Audiobook is Good TO READ!

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I bought this text after using and learning about Professor Harrell's contributions through the literature and through the R and S computing communities. Others have written how wonderful Professor Harrell's software is to use, and how carefully he has thought about the entire cycle of doing statistics, including transparent analyses and publication using LaTeX.But the book is a joy, balancing theoretical concerns, a masterful selection of literature, and solid assessments and presentations of actual examples. Professor Harrell demonstrates how powerful these techniques are, and the material moving them. These insights are key in a time when increasingly people want to just dump numbers into a package and get something out of it, an analytical behavior often justified by cost and time pressures. (If it's wrong results you want, it's trivially easy to get those, even if they LOOK wrong.) Professor Harrell shows it needn't be that hard, with tools from the CRAN (the R community source), many good ones which he and colleagues contributed. But he also shows that it's important to keep an eye on the parts of these, and be wary of pitfalls.Professor Harrell is also candid in his assessments, even after giving enthusiasts for a technique he critiques the benefit of the doubt. I find his comparison of cross-validation with bootstrap validation wonderful, and his discussion of standard assessments of models like R^2 refreshing.Check out his lectures, too:[...][...][...]

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