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Bayesian Computation with R (Use R)
There has been a dramatic growth in the development and application of Bayesian inferential methods Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods. Also the book is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book. .
Price: $39.04
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Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques in the Social Sciences)
"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high."
--Short Book Reviews from the International Statistical Institute "The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research."
--TED GERBER, Sociology, University of Arizona "Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."
--PAUL SWANK, Houston School of Nursing, University of Texas, Houston Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as: * An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III: * New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case
* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model
* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13) The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.
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Price: $89.50
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Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities
The hierarchical modelling framework represents a powerful and flexible framework for modelling and inference about ecological processes. It admits an explicit and formal representation of the data model into constituent components for observations and ecological process. The model for the ecological process of interest (the process model"), describes variation (spatial, temporal, etc..) in the ecological process that is the object of inference. This process is manifest in some (typically unobservable, or only partially so) state variable, say z(i,t), e.g., abundance or occurrence at some point in space (i) and time (t). Whereas the model for the observations conditional on the ecological process (the "observation model"), describes the probabilistic mechanisms by which the data are obtained. Whereas almost all classical methods focus exclusively on models that describe the sampling process, through the closely related probability distribution [data|parameters], the incorporation of these two component models into a single unified model (referred to as a hierarchical or state-space model) results in a generic and flexible strategy for conducting inference about population and community structure from biological sampling data. In particular, while the [data,process,parameters] model may be very complex, the two component sub-models are typically very simple, even for some very complex data structures. This yields surprisingly simple solutions to some very complex problems. Examples include: (1) Hierarchical models of simple counts. (2) Modelling individual heterogeneity in capture-recapture models. (3) Estimating community structure by modelling occurrence of species. * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) * Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS * Computing support provided as technical appendices in an online companion site.
Price: $59.96
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HLM 6: Hierarchical Linear and Nonlinear Modeling
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Hierarchical Modeling and Analysis for Spatial Data (Monographs on Statistics and Applied Probability)
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..
Price: $67.96
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Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (Interdisciplinary Statistics)
Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. The book explores a range of topics in Bayesian inference and modeling, including Markov chain Monte Carlo methods, Gibbs sampling, the Metropolis–Hastings algorithm, goodness-of-fit measures, and residual diagnostics. It also focuses on special topics, such as cluster detection; space-time modeling; and multivariate, survival, and longitudinal analyses. The author explains how to apply these methods to disease mapping using numerous real-world data sets pertaining to cancer, asthma, epilepsy, foot and mouth disease, influenza, and other diseases. In the appendices, he shows how R and WinBUGS can be useful tools in data manipulation and simulation. Applying Bayesian methods to the modeling of georeferenced health data, Bayesian Disease Mapping proves that the application of these approaches to biostatistical problems can yield important insights into data..
Price: $57.56
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Advanced QoS for Multi-Service IP/MPLS Networks
Advanced QoS for Multi-Service IP/MPLS Networks is the definitive guide to Quality of Service (QoS), with comprehensive information about its features and benefits. Find a solid theoretical and practical overview of how QoS can be implemented to reach the business objectives defined for an IP/MPLS network. Topics include standard QoS models for IP/MPLS networks, essential QoS features, forwarding classes and queuing priorities, buffer management, multipoint shared queuing, hierarchical scheduling, and rate limiting. This book will enable you to create a solid QoS architecture/design, which is mandatory for prioritizing services throughout the network..
Price: $25.46
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Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies
Supply Chain Management, Enterprise Resources Planning (ERP), and Advanced Planning Systems (APS) are important concepts in order to organize and optimize the flow of goods, materials, information and funds. This book, already in its fourth edition, gives a broad and up-to-date overview of the concepts underlying APS. Special emphasis is given to modeling supply chains and implementing APS successfully in industry. Understanding is enhanced by several case studies covering a wide range of industrial sectors. The fourth edition contains updated material, rewritten chapters and additional case studies. .
Price: $59.75
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Multivariate and Megavariate Data Analysis Advanced Applications and Method Extensions (Part II)
This second volume has two parts, the first with specialized applications of multi- and mega-variate analysis, namely:QSAR (quantitative structure-activity relationships) describes how series of molecular structures can be translated to quantitative data and how these data then are used to model and predict biological activity measurements made on the corresponding molecules. Chapters on how the QSAR concept applies in peptide QSAR, lead finding and optimization, combinatorial chemistry, and chem-and bio-informatics, are included.The multi- and megavariate analysis of "omics" data, has a special chapter, i.e., data from metabonomics, proteomics, genomics and other areas.Then follow six chapters on extensions of the basic projection methods (PCA and PLS):Orthogonal PLS (OPLS) showing how a PLS model can be "rotated" so that all y-related information appears in the first component, which facilitates the model interpretation.Hierarchical modeling, both PC and PLS, allowing variables of different types to be handled in separate blocks, which greatly simplifies the handling of datasets with very many variables.Non-linear PLS describes various approaches to the modeling of non-linear relationships between predictors X and responses Y.The Image Analysis chapter shows how multivariate analysis applies to the analysis of series of digital images.Data Mining and Integration has a discussion of how to get useful information out of large and complicated data sets, and how to manage and organize data in complex investigations.The second volume ends with a chapter on preference and sensory data, followed by an appendix summarizing the multivariate approach, statistical notes, and references..
Price: $95.00
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