Book Description
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes.
Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.
The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
From the reviews:
"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered."
Journal of Biopharmaceutical Statistics, 2005
"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book"
Technometrics, February 2006
"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally."
Journal of the American Statistical Association, March 2006
Customer Reviews:
very good book, compact but comprehensive.......2007-05-12
This book covers a wide range of topics in Biostatistics, in a comprehensive, but not overwhelming way. In my opinion this book has the potential of being useful to a broad audience, from Statisticians to other professionals who do health related research.
Excellent book ..........2007-01-09
A very specific book, with a lot of details for a statistitian
Book Description
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.
Customer Reviews:
Flawed but well-explained.......2007-07-16
"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.
Book Description
Wiley Series in Probability and Statistics
A modern perspective on mixed models
The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data.
As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features:
* A review of the basics of linear models and linear mixed models
* Descriptions of models for nonnormal data, including generalized linear and nonlinear models
* Analysis and illustration of techniques for a variety of real data sets
* Information on the accommodation of longitudinal data using these models
* Coverage of the prediction of realized values of random effects
* A discussion of the impact of computing issues on mixed models
Download Description
Wiley Series in Probability and Statistics
A modern perspective on mixed models
The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data.
As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features:
* A review of the basics of linear models and linear mixed models
* Descriptions of models for nonnormal data, including generalized linear and nonlinear models
* Analysis and illustration of techniques for a variety of real data sets
* Information on the accommodation of longitudinal data using these models
* Coverage of the prediction of realized values of random effects
* A discussion of the impact of computing issues on mixed models
Customer Reviews:
Very good textbook for the statistic model.......2005-02-05
This is a very good textbook. Since it covers most of important topics in the short pages. Authors assume that readers have the good background in the linear model. So if you have good background in linear model and statistic inference this will be the wonderful book for the statistic student. This is only one problem of this book. It cost toooo much for a poor student! Thus I take one point out.
excellent new book covering a wide variety of models.......2001-12-26
This is a very recent and authoritative treatment of classical parametric models, starting with the general linear model and extending to generalized linear models, linear mixed models and finally to generalized linear mixed models. It also has applciations to longitudinal data analysis and prediction problems. Heavy on theory and matrix algebra but not loaded with applications. Good for a graduate course in statistics especially for Ph.D. students. It is concise covering a large range of topics in only 310 pages. An interesting feature is a chapter on computing that deals with Markov chain Monte Carlo methods in some detail.
There is also a brief chapter on nonlinear models (only 5 pages) that includes an example of corn photosynthesis and also the important application to pharmacokinetic models.
The emphasis is on maximum likelihood estimation and its extensions (e.g. restricted maximumlikelihood and penalized likelihood and quasi-likelihood). The authors provide an interesting perspective on the non-applicability of analysis of variance techniques in some mixed effects models.
Book Description
This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.
The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data analysis. The book is suitable for a course in a M.S. program in statistics, provided that the section of further results and technical notes in each of the first four chapters is skipped. If these four sections are included, the book may be used for a course in a Ph. D. program in statistics. A first course in mathematical statistics, the ability to use computers for data analysis, and familiarity with calculus and linear algebra are prerequisites. Additional statistical courses such as regression analysis and a good knowledge about matrices would be helpful.
Book Description
This digital document is a journal article from Insurance Mathematics and Economics, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Over the last decade the use of generalized linear models (GLMs) in actuarial statistics has received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh and Nelder [McCullagh, P., Nelder, J.A., 1989. Generalized linear models. In: Monographs on Statistics and Applied Probability. Chapman and Hall, New York]. Traditional GLMs however model a sample of independent random variables. Since actuaries very often have repeated measurements or longitudinal data (i.e. repeated measurements over time) at their disposal, this article considers statistical techniques for modelling such data within the framework of GLMs. Use is made of generalized linear mixed models (GLMMs) which model a transformation of the mean as a linear function of both fixed and random effects. The likelihood and Bayesian approaches to GLMMs are explained. The models are illustrated by considering classical credibility models and more general regression models for non-life ratemaking in the context of GLMMs. Details on computation and implementation (in SAS and WinBugs) are provided.
Book Description
This digital document is a journal article from Journal of Empirical Finance, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
The aims of this paper are threefold. First, we highlight the usefulness of generalized linear mixed models (GLMMs) in the modelling of portfolio credit default risk. The GLMM-setting allows for a flexible specification of the systematic portfolio risk in terms of observed fixed effects and unobserved random effects, in order to explain the phenomena of default dependence and time-inhomogeneity in historical default data. Second, we show that computational Bayesian techniques such as the Gibbs sampler can be successfully applied to fit models with serially correlated random effects, which are special instances of state space models. Third, we provide an empirical study using Standard and Poor's data on U.S. firms. A model incorporating rating category and sector effects, and a macroeconomic proxy variable for state-of-the-economy suggests the presence of a residual, cyclical, latent component in the systematic risk.
Book Description
This digital document is a journal article from Accident Analysis and Prevention, published by Elsevier in 2005. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
In recent years, there has been a renewed interest in applying statistical ranking criteria to identify sites on a road network, which potentially present high traffic crash risks or are over-represented in certain type of crashes, for further engineering evaluation and safety improvement. This requires that good estimates of ranks of crash risks be obtained at individual intersections or road segments, or some analysis zones. The nature of this site ranking problem in roadway safety is related to two well-established statistical problems known as the small area (or domain) estimation problem and the disease mapping problem. The former arises in the context of providing estimates using sample survey data for a small geographical area or a small socio-demographic group in a large area, while the latter stems from estimating rare disease incidences for typically small geographical areas. The statistical problem is such that direct estimates of certain parameters associated with a site (or a group of sites) with adequate precision cannot be produced, due to a small available sample size, the rareness of the event of interest, and/or a small exposed population or sub-population in question. Model based approaches have offered several advantages to these estimation problems, including increased precision by ''borrowing strengths'' across the various sites based on available auxiliary variables, including their relative locations in space. Within the model based approach, generalized linear mixed models (GLMM) have played key roles in addressing these problems for many years. The objective of the study, on which this paper is based, was to explore some of the issues raised in recent roadway safety studies regarding ranking methodologies in light of the recent statistical development in space-time GLMM. First, general ranking approaches are reviewed, which include naive or raw crash-risk ranking, scan based ranking, and model based ranking. Through simulations, the limitation of using the naive approach in ranking is illustrated. Second, following the model based approach, the choice of decision parameters and consideration of treatability are discussed. Third, several statistical ranking criteria that have been used in biomedical, health, and other scientific studies are presented from a Bayesian perspective. Their applications in roadway safety are then demonstrated using two data sets: one for individual urban intersections and one for rural two-lane roads at the county level. As part of the demonstration, it is shown how multivariate spatial GLMM can be used to model traffic crashes of several injury severity types simultaneously and how the model can be used within a Bayesian framework to rank sites by crash cost per vehicle-mile traveled (instead of by crash frequency rate). Finally, the significant impact of spatial effects on the overall model goodness-of-fit and site ranking performances are discussed for the two data sets examined. The paper is concluded with a discussion on possible directions in which the study can be extended.
Book Description
This digital document is a journal article from Agriculture, Ecosystems and Environment, published by Elsevier in 2005. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
A multi-soil study was conducted in Denmark including 29 sites, 8 classified as 'organic', 11 as 'conventional with manure and synthetic fertilisers' and 10 as 'conventional with synthetic fertilisers'. The variability of fungal abundance within the three farming systems and the long-term effects of different farming systems on fungal propagules in soil were evaluated. Fungal abundance showed large variations within all three farming systems and this variability reduced the possibility to obtain general conclusions on fungal composition in soils under different farming systems. This was illustrated by the results on total propagule numbers of filamentous fungi and yeasts. Penicillium spp. and Gliocladium roseum were more abundant under organic than conventional farming, while Trichoderma spp. were most abundant in conventionally farmed soils with synthetic fertilisers. These results were not altered after adjusting for possible differences in basic soil properties like total C and N, extractable P, CEC, base saturation and soil density. The paper discusses whether the differences in fungal abundance are characteristics of a farming system itself or associated with certain management factors being more prevalent in one farming system than the other.
Average customer rating:
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Generalized Linear Mixed Models (Regional Conference Series)
Charles E. McCulloch
Manufacturer: Institute of Mathematical Statistics
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ASIN: 0940600544 |
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