Bayes and Empirical Bayes Methods for Data Analysis, Second Edition Review

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(More customer reviews)This book features a deep and focused lesson on Bayes and Empirical Bayes Methods. It goes through the key topics as conjugate priors, MCMC methods (non iteratives and iteratives as the well known Gibbs samplining and metropolitis hastings algorithms), model selection methods (as bayes factor) and issues related as model robusteness.
The Approach is increasingly formal and deeply complex, allowing for getting the basics or diving into more complex knowledge according to your former background. You need at least a good understanding of Frequentist statistic to be able to follow the reasonings. Each chapter allow you to stop at some point without losing the thread. Last part of the book is in fact deep knowledge demanding.
The most interesting point of this book according to my very limited statistics background is that it makes good comparations with the frequentist approach (classical approaches as confidence intervals and point estimators), checking performance of either method. Even, it features some combination of both approaches getting some bayessian intervals.
As a negative point, I would say that examples are hard to follow for someone with limited bakground and too much complex. They really do not clear me up enough.
All in all, is a very profitable book for jumping into bayesian methods.

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In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. Building on the first edition of their popular text, Carlin and Louis introduce these methods, demonstrate their usefulness in challenging applied settings, and show how they can be implemented using modern Markov chain Monte Carlo (MCMC) methods. Their presentation is accessible to those new to Bayes and empirical Bayes methods, while providing in-depth coverage valuable to seasoned practitioners.With its broad appeal as a text for those in biomedical science, education, social science, agriculture, and engineering, this second edition offers a relatively gentle and comprehensive introduction for students and practitioners already familiar with more traditional frequentist statistical methods. Focusing on practical tools for data analysis, the book shows how properly structured Bayes and EB procedures typically have good frequentist and Bayesian performance, both in theory and in practice.

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Bayesian Methods for Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) Review

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(More customer reviews)I like this book a lot. It's not the book that I would've written, and that's a good thing. Buying Carlin and Louis along with our book will give you two perspectives on applied Bayesian statistics as it is practiced in the 21st century. Compared to our book, Carlin and Louis offer the following:
- Discussion of the debates over Bayesianism within the statistical community, culminating in chapter 5, which covers the links between Bayes, empirical Bayes, and frequentist methods of evaluating statistical procedures.
- A crisp presentation of Bayesian computation (chapter 5), which offers a different perspective than ours.
- A chapter on experimental design including several biomedical examples. This chapter should be useful to a lot of people, I think.
- Near the end of the book, discussion of several classes of models--longitudinal analysis, survival analysis, spatial models, clinical trials, and others--where I often think, "What's would a Bayesian do here?"
I don't think Carlin and Louis have made our own Bayesian Data Analysis obsolete but I do think their book is a great complement to ours, with a slightly different perspective, strong coverage of the theoretical issues of point and interval estimation, and a bunch of compelling biomedical examples.

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Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition New data examples, corresponding R and WinBUGS code, and homework problems Explicit descriptions and illustrations of hierarchical modeling-now commonplace in Bayesian data analysisA new chapter on Bayesian design that emphasizes Bayesian clinical trialsA completely revised and expanded section on ranking and histogram estimationA new case study on infectious disease modeling and the 1918 flu epidemicA solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem-available both electronically and in printIdeal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.

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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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Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ several data sets throughout that cover a range of study designs, variable types, and missing data issues.
The book first reviews modern approaches to formulate and interpret regression models for longitudinal data. It then discusses key ideas in Bayesian inference, including specifying prior distributions, computing posterior distribution, and assessing model fit. The book carefully describes the assumptions needed to make inferences about a full-data distribution from incompletely observed data. For settings with ignorable dropout, it emphasizes the importance of covariance models for inference about the mean while for nonignorable dropout, the book studies a variety of models in detail. It concludes with three case studies that highlight important features of the Bayesian approach for handling nonignorable missingness.
With suggestions for further reading at the end of most chapters as well as many applications to the health sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.

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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Professor Gelman has edited a book containing 29 articles dealing primarily with real applications of Bayesian methods for causal inference and the treatment of incomplete data.
It contains a collection of the best work in applied statistics by prominent statisticians. In addition to learning the wide variety of problems that have been solved using the Bayesian approach (particularly in the medical field) the reader can learn and appreciate the power and ease of interpretation of Bayesian results.

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This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data.
Key features of the book include:
Comprehensive coverage of an imporant area for both research and applications.
Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques.
Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference.
Includes a number of applications from the social and health sciences.
Edited and authored by highly respected researchers in the area.

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Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

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(More customer reviews)I've bought several spatial statistics books over the years and found they generally fall into one of two categories; oversimplified or cover-to-cover matrix notation, neither of which is very useful for my research. However, this book is "just right," bridging these two extremes. It briefly covers the basics of both point and areal analysis, then gives the reader the tools for more advanced (i.e., realistic) analysis. They devote a chapter to Bayesian basics, which is needed for the last 4 or 5 chapters. The last few chapters weave together a detailed discussion on a variety of hierarchical models and current published results. Most importantly this book offers quite a bit of the necessary R and Winbugs code. Although many of their examples are from the public health world, the techniques and code are easily adapted to natural resource data - my personal focus.

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Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and data analysis for spatial and spatio-temporal data. Starting with overviews of the types of spatial data, the data analysis tools appropriate for each, and a brief review of the Bayesian approach to statistics, the authors discuss hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice (areal data) modeling. They then consider the problem of spatially misaligned data, methods for handling multivariate spatial responses, spatio-temporal models, and spatial survival models. The final chapter explores a variety of special topics, including spatially varying coefficient models.This book provides clear explanations, plentiful illustrations --some in full color--a variety of homework problems, and tutorials and worked examples using some of the field's most popular software packages.. Written by a team of leaders in the field, it will undoubtedly remain the primary textbook and reference on the subject for years to come.

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Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities Review

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(More customer reviews)Royle & Dorazio (2008): A truly synthetic overview
This book not only illustrates, and presents R and WinBUGS code for, plenty of methods for inference about distribution and abundance in animal and plant populations and communities; it does much more. It presents a truly synthetic overview of these methods and makes the reader understand how they relate to each other. At the same time, the authors succeed extremely well in teaching a modern, "organic way" of statistical modeling -- where one first thinks hard about how the observed data might have arisen via a combination of stochastic processes (the book is about hierarchical models, remember) and then builds a custom statistical model for exactly those processes. This combination of presenting a unifying synthesis of a vast array of methods and showing how to model a study system organically in my view is unique among the currently available statistical ecology books.

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A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods.This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures.The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution* abundance models based on many sampling protocols, including distance sampling* capture-recapture models with individual effects* spatial capture-recapture models based on camera trapping and related methods* population and metapopulation dynamic models* models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants)* Development of classical, likelihood-based procedures for inference, as well asBayesian methods of analysis* Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS* Computing support in technical appendices in an online companion web site

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Bayesian Modeling Using WinBUGS (Wiley Series in Computational Statistics) Review

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(More customer reviews)I try to read at least a couple of statistics books every year and this was one of them for 2009. So far I have been really impressed. If you want a complete introduction to Bayesian statistics, then buy this book. You will find a balanced blend of theory, applications, and the use of the WinBUGS software package all under one roof. The book's treatment of models for count data is notable. Ntzoufras has a nice way of expressing himself that makes the reading move along. I would have no compunction at all about using this book to teach a M.S.-level course for statistics majors. If you are an ecologist, say, then you should probably have both a probability and mathematical statistics course under your belt to fully absorb all that is going on.

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A hands-on introduction to the principles of Bayesian modeling using WinBUGS
Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles.
The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including:

Markov Chain Monte Carlo algorithms in Bayesian inference

Generalized linear models

Bayesian hierarchical models

Predictive distribution and model checking

Bayesian model and variable evaluation

Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site.
Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.

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