Negative Binomial Regression Review

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(More customer reviews)The first edition of this book was one of the first on this topic. The text is is very comprehensive, covering count models in general, the common Poisson regression model and its generalization to over-and-under dispersion with all the various forms of the negative binomial regression model. The Poisson distribution has the property that its mean and variance are the same. When sample estimates of variance are significantly higher (lower) than the estimated mean, the model is said to be overdispersed (underdispersed).
The main additions in the second edition of the book are the advances in software to estimate parameters of the various negative binomial models. Hilbe describes the currently available software in SAS, SPSS and STATA as well as the econometric package LIMDEP.
The book covers the historical development of the negative binomial regression model. It is primarily an applied text with numerous examples and demonstration of the various software products. As with all of Joe Hilbe's books, this text is thorough and scholarly with an extensive list of references. Important theorems and other theoretical results are given but are presented to be imformative rather than to develop and teach the theory. The text is well-written and for the most part easy to understand. Emphasis is on computation and goodness of fit of the models. Although both overdispersion and underdispersion are covered overdispersion is emphasized as Hilbe sees it as the most common departure from the Poisson model.

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This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with examples of their construction, application, interpretation and evaluation. Complete Stata and R codes are provided throughout the text, with additional code (plus SAS), derivations and data provided on the book's website. Written for the practising researcher, the text begins with an examination of risk and rate ratios, and of the estimating algorithms used to model count data. The book then gives an in-depth analysis of Poisson regression and an evaluation of the meaning and nature of overdispersion, followed by a comprehensive analysis of the negative binomial distribution and of its parameterizations into various models for evaluating count data.

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Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Chapman & Hall/CRC Texts in Statistical Science) Review

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(More customer reviews)"Extending the Linear Model with R" is a "sequel" of sorts to the impressive "Linear Models with R" also written by Faraway. It assumes a basic knowledge of R (you don't have to be an expert) and a decent understanding of linear models. If you don't have that background, then I would start with the before-mentioned "Linear Models with R". If you read and understood that book, then you should be more than prepared for this one.
This book covers extensions of the linear model including Generalized Linear Models (GLM's), Mixed and Random Effects Models, Nonparametric Regression Models, Additive Models (including GAM's - Generalized Additive Models), and it contains a brief introduction to Regression Trees and Neural Networks. The biggest focus is on Generalized Linear Models. The book is fairly thorough, though not exactly comprehensive, in covering the topic of GLM's and specific commonly used GLM's. The material is very well-explained and easy to follow and they do a good job at integrating code, examples, and graphs in a way that facilitates understanding of both statistical concepts regarding GLM's and also the implementation of these concepts in R. The code is especially useful and it covers most things in R that you will need for this topic, at least those available from CRAN. The book is not very rigorous regarding theory, but that only makes the book easier to read and more practical. However, I do have one complaint regarding this section. The author spends several chapters discussing various commonly used GLM's and THEN finally gets around to defining what a GLM is and covering the basic theory. This seems backwards to me and for this reason I wouldn't read the chapters in order. Also, due to the late coverage of some of the basic theories, we don't get to see the implementation and analysis of certain sub-topics (e.g. leverage and influence) in the early examples.
Mixed and Random Effects models are second in terms of attention received. The organization is better and the explanations and code integration continue to be handled well. Nonparametric Regression and Additive Models only receive one chapter apiece, but both chapters are extremely informative and they are well-explained like the rest of the book. I was especially happy to see the coverage of GAM's (it's very short but useful) since it is a moderately recent topic (1990) and many similar books only make a brief mention of them (hey, GAM's exist) if they are mentioned at all. The chapter on Regression Trees is short, but again they make sure to cover many of the important sub-topics with clarity. The Neural Networks chapter is skimpy and you won't learn much, but it was an unexpected bonus so I can't take off points for that.
Do note that this book takes a regression approach throughout, so look elsewhere for an ANOVA perspective. The book is short with plenty of room left to talk about other topics. Thus, I would have liked to see a second part devoted to an ANOVA approach since I'm the kind of person who hates having to thumb through countless books, but they are open about the book's scope so I can't really complain.
Okay, one more complaint. I would have greatly liked to see an appendix of the R functions used throughout the book with short descriptions and references to where in the book you can find the function being discussed. R Help isn't bad, so it's not a tragic omission, but it still would have been nice.
In summary, this book is extremely useful if you plan on using extensions of linear models with R. Flaws aside, it receives my recommendation.

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Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site at www.stat.lsa.umich.edu/~faraway/ELM holds all of the data described in the book.Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.

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Logistic Regression: A Self-Learning Text Review

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(More customer reviews)When Kleinbaum entitles his book "a self-learning text", this is TRUE ! I'm sure anyone can learn logistic regression with this book. It is cristal-clear, very progressive, with real-data examples... If the best teachers are those who make you feel you're intelligent, certainly the author must be a good teacher... because his book is ! I do recommend it warmly to anyone who has to teach (like me) or learn logistic regression.

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This is the second edition of this text on logistic regression methods. As in the first edition, each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This second edition includes five new chapters and an appendix. The new chapters are: Chapter 9. Polytomous Logistic Regression Chapter 10. Ordinal Logistic Regression Chapter 11. Logistic Regression for Correlated Data Chapter 12. GEE Examples Chapter 13. Other Approaches for Analysis of Correlated Data Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11-13 extend logistic regression to generalized estimating equations (GEE) and other methods for analyzing correlated response data. The appendix "Computer Programs for Logistic Regression" provides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The software packages considered are SAS Version 8.0, SPSS Version 10.0 and STATA Version 7.0.

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A Practical Guide to Ecological Modelling: Using R as a Simulation Platform Review

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(More customer reviews)This is an awesome book, primarily about modeling, less about R. There are many "introduction to R" books available, perhaps too many.
This book and its accompanying software in the several R packages are an excellent way to both learn the material, and to explore other related problems.
Naturally, the course is primarily what you bring to it. Soetaert and Hermann provide a comprehensive introduction, heavily illustrated by examples. The mathematics is excellent, and essential.

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Mathematical modelling is an essential tool in present-day ecological research. Yet for many ecologists it is still problematic to apply modelling in their research. In our experience, the major problem is at the conceptual level: proper understanding of what a model is, how ecological relations can be translated consistently into mathematical equations, how models are solved, steady states calculated and interpreted. Many textbooks jump over these conceptual hurdles to dive into detailed formulations or the mathematics of solution. This book attempts to fill that gap. It introduces essential concepts for mathematical modelling, explains the mathematics behind the methods, and helps readers to implement models and obtain hands-on experience. Throughout the book, emphasis is laid on how to translate ecological questions into interpretable models in a practical way.The book aims to be an introductory textbook at the undergraduate-graduate level, but will also be useful to seduce experienced ecologists into the world of modelling. The range of ecological models treated is wide, from Lotka-Volterra type of principle-seeking models to environmental or ecosystem models, and including matrix models, lattice models and sequential decision models. All chapters contain a concise introduction into the theory, worked-out examples and exercises. All examples are implemented in the open-source package R, thus taking away problems of software availability for use of the book. All code used in the book is available on a dedicated website.

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Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics) Review

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(More customer reviews)Back in 2000 Stephen Fienberg gave a talk at the University of California at Irvine on the 2000 census and his book "Who Counts". After the talk I went to dinner with him, my colleague Bob Newcomb and Anita Iannucci. Driving to dinner Bob ask Steve for a recommendation on a multivariate textbook. A number of choice were mentioned. Bob's favorite was Cooley and Lohnes but that was a bit dated. He was definitely looking for an applied text and not a theoretical one. I learned my multivariate analysis out of the first edition of Ted Anderson's book. But that is traditional multivariate Gaussian theory and is not at all an applied text. I always liked Gnanadesikan's book and I mentioned that. Srivastava and carter is an applied text that I like and there are many other choices.
I don't recall many of Fienberg's suggestions but I do distinctly recall that he did say that now you can teach it as a special case of the generalized linear models. The idea seemed to make sense to me but I couldn't picture the details. This book is apparently the book Fienberg had in mind. He might have been thinking about the first edition because this second edition was not out then.
The book is very applied and modern and covers many important topics for biostatisticians. Coverage includes multicategorical responses, semi and nonparametric modelling, time series and longitudinal data, random effects models, state space models including Kalman Filters and nonlinear models, and survival analysis. This is not traditional multivariate data but covers many type of multivariate data and models that do not fit the standard multivariate Gaussian theory.
Chapter 4 on selecting and checking models seems to deal with the classical linear models taking a non-standard approach through the methods of generalized linear models.
Excellent text for an applied course and for a reference book. It also covers hidden Markov models and Bayesian methods (including the MCMC implementation and the WinBugs software).


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The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.

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Multidimensional Item Response Theory (Statistics for Social and Behavioral Sciences) Review

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(More customer reviews)The MIRT sub-field of psychometrics has for years labored in obscurity, due in no small part to the inability of its practitioners and students to understand each other and master each other's algorithms and models. Reckase, one of the field's leaders, takes a bold step in correcting the situation. Excellently researched, clearly written, logically presented, fair and balanced, Reckase summarizes the foundations of probabilistic unidimensional models and shows how they generalize, so that persons (test examinees) and items (test questions) can be represented as points (vectors) floating around in a multidimensional space.
This is not a book for the field practitioner or the casual researcher. It does not skip over the math, and the math is hard-core. Nonetheless, it is surprisingly readable. The reader will be pleased to find himself following the gist of Reckase's explanations without difficulty, even when the mathematical details are too much.
To appreciate this work, it is important to know why MIRT is important. Unfortunately, Reckase never tells us. We understand that MIRT is motivated by the fact that items and tests are complex, that they embody multiple dimensions, that therefore a multidimensional model is necessary. This hardly touches the surface. As the fantastic drama of the Netflix contest revealed (a recently resolved contest to win $1 m. for best predicting movie ratings), we live in a world of psychological profiling and prediction, a world populated by weird and incredible mathematical models that touch on every aspect of life -- from selecting food at Safeway, to renting movies, to profiling terrorists, to guiding teacher instructional decisions, to training computers to read and understand text and recognize the spoken word. None of that is in this book. The great divide between educational psychometrics and "data mining" or "knowledge discovery" has yet to be crossed. MIRT is the subfield within educational psychometrics that will ultimately bridge that divide.
On the theory side, Reckase does not conceal his differences with the "Rasch School" of psychometrics (of which I am a member) regarding the purpose of educational measurement and modeling, though he is obviously well-versed in Rasch models and presents them well, including their MIRT flavors. He sees the purpose of a model to be "descriptive" (to describe the data closely), whereas Rasch theorists see the purpose of a model to be "prescriptive" (to prescribe the conditions under which data yield true measures, i.e., measures that are most likely to reproduce across datasets regardless of person and item samples). The models that Reckase speaks about with the confidence of personal knowledge are "descriptive" in this sense.
Due perhaps to his preference for descriptive models, I found there were certain questions that Reckase did not seem to spend time on, questions that are huge for me:
1. How well do MIRT models handle small sample sizes?
2. How do they handle missing data, whether randomly or non-randomly missing?
3. To what degree are the person and item parameters invariant across samples? Can I cherry-pick my samples and get different parameters?
These are the sorts of questions Rasch people are always asking and where the Rasch model, properly used, has much to offer.
I also found myself looking in vain for discussion of Rasch's "specific objectivity" property as relates to MIRT, often called the "invariance" property. I learned that Reckase means something else entirely by the same word. In the Rasch world, "invariance" means that item and person parameters, and the resulting response probabilities, are invariant across samples, that persons will obtain the same relative measures regardless of what items they are administered so long as the items embody the same dimension. For Reckase, "invariance" means that the origin and orientation of the coordinate system can be moved without affecting the response probabilities. It's got nothing to do with samples. So, in the end, I still don't know what, if any, invariance properties the various MIRT models discussed in the book possess, defining "invariance" in the Rasch sense as invariance across person and item samples.

But those are my problems, not Reckase's. This book is a significant step forward in the maturation of an extraordinarily important, but little known, field.

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First thorough treatment of multidimensional item response theoryDescription of methods is supported by numerous practical examplesDescribes procedures for multidimensional computerized adaptive testing

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Regression Modeling Strategies Review

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(More customer reviews)Frank Harrell is a Professor who does a lot of consulting in medical research. This book covers a wide variety of topics in regression analysis including many advanced techniques including data reduction, smoothing techniques, variable selection, transformations, shrinkage methods, tree-based methods and resampling. But note the title "Regression Modeling Strategies". Unlike most advanced texts in regression this book emphasizes modeling strategies. So the focus is on things like variable selection and other techniques to avoid overfitting models and diagnostics to look for violations in assumptions such as variance homogeneity or normality and independence of residuals, or stability problems like colinearity.
The book covers an extensive collection of modern techniques for exploratory data analysis. Inferential methods are also considered and he deals appropriately with important issues (particularly for medical research) such as imputation of missing values. Many examples are considered and illustrated in S-PLUS.
Harrell also provides many rules of thumb based on his own experience building models. A lot of the techniques are illustrated using data from the Titanic where it is interesting to see which factors affected the probability of survival. My only disappointment was that there is perhaps too much emphasis on this one particular data set.
A standard regression text would be expected to include linear and nonlinear regression. Harrell goes much deeper including nonparametric regression, logistic regression and survival models (e.g. the Cox proportional hazards model).


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Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about 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 dealing with 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. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

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Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

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(More customer reviews)Issues of missing data in longitudinal studies are very important in the design and analysis of clinical trials. This is such an important statistical topic, that many excellent books have been written about it. One of the earliest and a landmark text was the book by Rubin and Little which was recently updated in the second edition. Mixed linear models for longitudinal data provide an effective method for dealing with several types of missingness as does multiple imputation. Pattern mixture models are also very useful. Molenberghs and Kennard, Verbeke and Molenberghs and Rubin all cover these topic well in their excellent texts.
What then is the advantage of this text by Daniels and Hogan?
1. It is slightly more current than the others
2. It combines theory and application very nicely
3. A series of seven real data sets from real clinical trials and epidemiologic studies are presented up front in Chapter 1 and used throughout to illustrate practical advantages and disadvantages of the various techniques covered in the latter chapters
4. It covers Bayesian modeling and sensitivity analysis in more depth that most of its competitors
Only Molenberghs and Kennard match it in the depth of coverage on theory and applications. But they do not provide the coverage of Bayesian methods the way Daniels and Hogan do.
For these reasons I recommend this book to the practicing biostatisticians working on clinical trials even if the texts listed below are alresdy on their bookshelves.
I) Diggle, P. J., Heagerty, P., Liang, K.-Y. and Zeger, S. L. (2002). "Analysis of Longitudinal Data" 2nd Edition. Oxfrod University Press, Oxford.
II) Fitzmaurice, G. M., Laird, N.M. and Ware, J. H. (2004). "Applied ongitudinal Analysis". John Wiley & Sons, New York.
III) Little, R. J. A. and Rubin, D. B. (2002) "Statistical Analysis with Missing Data" 2nd Edition, John Wiley & Sons, New York
IV) Molenberghs, G and Kennard, M. G. (2007). "Missing Data in Clinical Studies" John Wiley & Sons, Chichester.
V) Molenberghs, G. and Verbeke, G. (2005). "Models for Discrete Longitudinal Data". Springer-Verlag, New York.
VI) Pinheiro, J. C. and Bates, D. M. (2000). "Mixed Effects Models in S and S-Plus". Springer-Verlag, New York.
VII) Rubin, D. B. (1987). "Multiple Imputation for Nonresponse in Surveys" John Wiley & Sons, New York.
VIII) Tsiatis, A. A. (2006). "Semiparametric Theory and Missing Data" Dpringer-Verlag, New York.
IX) Verbeke, G and Molenberghs, G. (1997). "Linear Mixed Models in Practice: A SAS-Oriented Approach" Springer-Verlag, New York.
X) Verbeke, G and Molenberghs, G. (2000). "Linear Mixed Models for Longitudinal Data" Springer-Verlag, New York.


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Regression Modeling: Methods, Theory, and Computation with SAS Review

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(More customer reviews)As a doctoral student in public health, I am always looking for more texts to add to my professional library in statistics. This book not only covers some of the basic regression methods, but also many of the much more advanced methods that I have in classes. I really like how this text explains simply the ideas behind the different methods and when to use, as well as the SAS code and the explanation of the SAS code and output. The only downside is that I think the index could be a little longer - sometimes it is difficult to find a particular model though it actually is in the text.

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Applied Regression Modeling: A Business Approach Review

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(More customer reviews)There are so many great things about this book on applied regression that it is hard to know where to start. I'll mention six areas. First, the text teaches from the standpoint of someone learning rather than just presenting topics. The text goes carefully through examples that draw attention to what works and what does not work. Second, I like the chapter on extensions of regression. Third, I like the detailed steps of each hypothesis test along with the meaning of each step. Fourth, I like the thorough review of transformations of predictors and transformations of the dependent variable. Fifth, great treatment of categorical values. Sixth: good use of graphs to study whether the four assumptions of regression are met. Bottom line: if you study this book carefully and work through the examples, you will have a useful tool box to apply to a wide variety of business problems. If you are interested in this topic, I highly recommend this text. It's well worth the investment.

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An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression analysis to make informed decisions. Applied Regression Modeling: A Business Approach offers a practical, workable introduction to regression analysis for upper-level undergraduate business students, MBA students, and business managers, including auditors, financial analysts, retailers, economists, production managers, and professionals in manufacturing firms.The book's overall approach is strongly based on an abundant use of illustrations and graphics and uses major statistical software packages, including SPSS(r), Minitab(r), SAS(r), and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided, although Excel does not have a built-in capability to carry out all the techniques discussed.Applied Regression Modeling: A Business Approach offers special user features, including:* A companion Web site with all the datasets used in the book, classroom presentation slides for instructors, additional problems and ideas for organizing class time around the material in the book, and supplementary instructions for popular statistical software packages. An Instructor's Solutions Manual is also available.* A generous selection of problems-many requiring computer work-in each chapter with fullyworked-out solutions* Two real-life dataset applications used repeatedly in examples throughout the book to familiarize the reader with these applications and the techniques they illustrate* A chapter containing two extended case studies to show the direct applicability of the material* A chapter on modeling extensions illustrating more advanced regression techniques through the use of real-life examples and covering topics not normally seen in a textbook of this nature* More than 100 figures to aid understanding of the materialApplied Regression Modeling: A Business Approach fully prepares professionals and students to apply statistical methods in their decision-making, using primarily regression analysis and modeling. To help readers understand, analyze, and interpret business data and make informed decisions in uncertain settings, many of the examples and problems use real-life data with a business focus, such as production costs, sales figures, stock prices, economic indicators, and salaries. A calculus background is not required to understand and apply the methods in the book.

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