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Case Studies In Bayesian Statistical Modelling And Analysis

Author: Clair L. Alston
Publisher: John Wiley & Sons
ISBN: 1118394321
Size: 76.61 MB
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Provides an accessible foundation to Bayesian analysis usingreal world models This book aims to present an introduction to Bayesian modellingand computation, by considering real case studies drawn fromdiverse fields spanning ecology, health, genetics and finance. Eachchapter comprises a description of the problem, the correspondingmodel, the computational method, results and inferences as well asthe issues that arise in the implementation of theseapproaches. Case Studies in Bayesian Statistical Modelling andAnalysis: Illustrates how to do Bayesian analysis in a clear and concisemanner using real-world problems. Each chapter focuses on a real-world problem and describes theway in which the problem may be analysed using Bayesianmethods. Features approaches that can be used in a wide area ofapplication, such as, health, the environment, genetics,information science, medicine, biology, industry and remotesensing. Case Studies in Bayesian Statistical Modelling andAnalysis is aimed at statisticians, researchers andpractitioners who have some expertise in statistical modelling andanalysis, and some understanding of the basics of Bayesianstatistics, but little experience in its application. Graduatestudents of statistics and biostatistics will also find this bookbeneficial.

Applied Bayesian Modelling

Author: Peter Congdon
Publisher: John Wiley & Sons
ISBN: 1118895053
Size: 71.79 MB
Format: PDF, ePub
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This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.

Spatial And Spatio Temporal Geostatistical Modeling And Kriging

Author: José-María Montero
Publisher: John Wiley & Sons
ISBN: 1118762436
Size: 34.19 MB
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Statistical Methods for Spatial and Spatio-Temporal Data Analysis provides a complete range of spatio-temporal covariance functions and discusses ways of constructing them. This book is a unified approach to modeling spatial and spatio-temporal data together with significant developments in statistical methodology with applications in R. This book includes: Methods for selecting valid covariance functions from the empirical counterparts that overcome the existing limitations of the traditional methods. The most innovative developments in the different steps of the kriging process. An up-to-date account of strategies for dealing with data evolving in space and time. An accompanying website featuring R code and examples

Statistical Shape Analysis

Author: Ian L. Dryden
Publisher: John Wiley & Sons
ISBN: 1119072506
Size: 24.60 MB
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A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while retaining sufficient detail for more specialist statisticians to appreciate the challenges and opportunities of this new field. Computer code has been included for instructional use, along with exercises to enable readers to implement the applications themselves in R and to follow the key ideas by hands-on analysis. Statistical Shape Analysis: with Applications in R will offer a valuable introduction to this fast-moving research area for statisticians and other applied scientists working in diverse areas, including archaeology, bioinformatics, biology, chemistry, computer science, medicine, morphometics and image analysis .

Multivariate Density Estimation

Author: David W. Scott
Publisher: John Wiley & Sons
ISBN: 1118575482
Size: 57.27 MB
Format: PDF, Kindle
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Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis. The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features: Over 150 updated figures to clarify theoretical results and to show analyses of real data sets An updated presentation of graphic visualization using computer software such as R A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering More than 130 problems to help readers reinforce the main concepts and ideas presented Boxed theorems and results allowing easy identification of crucial ideas Figures in color in the digital versions of the book A website with related data sets Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.

Statistics For Spatio Temporal Data

Author: Noel Cressie
Publisher: John Wiley & Sons
ISBN: 1119243041
Size: 17.92 MB
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Winner of the 2013 DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes,bridging classic ideas with modern hierarchical statisticalmodeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winnersof the 2011 PROSE Award in the Mathematics category, for thebook “Statistics for Spatio-Temporal Data” (2011),published by John Wiley and Sons. (The PROSE awards, forProfessional and Scholarly Excellence, are given by the Associationof American Publishers, the national trade association of the USbook publishing industry.) Statistics for Spatio-Temporal Data has now beenreprinted with small corrections to the text andthe bibliography. The overall content and pagination of thenew printing remains the same; the difference comes inthe form of corrections to typographical errors, editing ofincomplete and missing references, and some updated spatio-temporalinterpretations. From understanding environmental processes and climate trends todeveloping new technologies for mapping public-health data and thespread of invasive-species, there is a high demand for statisticalanalyses of data that take spatial, temporal, and spatio-temporalinformation into account. Statistics for Spatio-TemporalData presents a systematic approach to key quantitativetechniques that incorporate the latest advances in statisticalcomputing as well as hierarchical, particularly Bayesian,statistical modeling, with an emphasis on dynamical spatio-temporalmodels. Cressie and Wikle supply a unique presentation thatincorporates ideas from the areas of time series and spatialstatistics as well as stochastic processes. Beginning with separatetreatments of temporal data and spatial data, the book combinesthese concepts to discuss spatio-temporal statistical methods forunderstanding complex processes. Topics of coverage include: Exploratory methods for spatio-temporal data, includingvisualization, spectral analysis, empirical orthogonal functionanalysis, and LISAs Spatio-temporal covariance functions, spatio-temporal kriging,and time series of spatial processes Development of hierarchical dynamical spatio-temporal models(DSTMs), with discussion of linear and nonlinear DSTMs andcomputational algorithms for their implementation Quantifying and exploring spatio-temporal variability inscientific applications, including case studies based on real-worldenvironmental data Throughout the book, interesting applications demonstrate therelevance of the presented concepts. Vivid, full-color graphicsemphasize the visual nature of the topic, and a related FTP sitecontains supplementary material. Statistics for Spatio-TemporalData is an excellent book for a graduate-level course onspatio-temporal statistics. It is also a valuable reference forresearchers and practitioners in the fields of applied mathematics,engineering, and the environmental and health sciences.

Combinatorial Methods In Discrete Distributions

Author: Charalambos A. Charalambides
Publisher: John Wiley & Sons
ISBN: 0471733172
Size: 19.21 MB
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A unique approach illustrating discrete distribution theory throughcombinatorial methods This book provides a unique approach by presenting combinatorialmethods in tandem with discrete distribution theory. This method,particular to discreteness, allows readers to gain a deeperunderstanding of theory by using applications to solve problems.The author makes extensive use of the reduction approach toconditional distributions of independent random occupancy numbers,and provides excellent studies of occupancy and sequentialoccupancy distributions, convolutions of truncated discretedistributions, and compound and mixture distributions. Combinatorial Methods in Discrete Distributions begins with abrief presentation of set theory followed by basic countingprinciples. Fundamental principles of combinatorics, finitedifferences, and discrete probability are included to give readersthe necessary foundation to the topics presented in the text. A thorough examination of the field is provided andfeatures: Stirling numbers and generalized factorial coefficients Occupancy and sequential occupancy distributions n-fold convolutions of truncated distributions Compound and mixture distributions Thoroughly worked examples aid readers in understanding complextheory and discovering how theory can be applied to solve practicalproblems. An appendix with hints and answers to the exercises helpsreaders work through the more complex sections. Reference notes areprovided at the end of each chapter, and an extensive bibliographyoffers readers a resource for additional information on specializedtopics.

Theory Of Preliminary Test And Stein Type Estimation With Applications

Author: A. K. Md. Ehsanes Saleh
Publisher: John Wiley & Sons
ISBN: 0471773743
Size: 76.76 MB
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Theory of Preliminary Test and Stein-Type Estimation withApplications provides a com-prehensive account of the theory andmethods of estimation in a variety of standard models used inapplied statistical inference. It is an in-depth introduction tothe estimation theory for graduate students, practitioners, andresearchers in various fields, such as statistics, engineering,social sciences, and medical sciences. Coverage of the material isdesigned as a first step in improving the estimates before applyingfull Bayesian methodology, while problems at the end of eachchapter enlarge the scope of the applications. This book contains clear and detailed coverage of basic terminologyrelated to various topics, including: * Simple linear model; ANOVA; parallelism model; multipleregression model with non-stochastic and stochastic constraints;regression with autocorrelated errors; ridge regression; andmultivariate and discrete data models * Normal, non-normal, and nonparametric theory of estimation * Bayes and empirical Bayes methods * R-estimation and U-statistics * Confidence set estimation

Introduction To Nonparametric Regression

Author: K. Takezawa
Publisher: John Wiley & Sons
ISBN: 0471771449
Size: 44.20 MB
Format: PDF, ePub
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An easy-to-grasp introduction to nonparametric regression This book's straightforward, step-by-step approach provides anexcellent introduction to the field for novices of nonparametricregression. Introduction to Nonparametric Regression clearlyexplains the basic concepts underlying nonparametric regression andfeatures: * Thorough explanations of various techniques, which avoid complexmathematics and excessive abstract theory to help readersintuitively grasp the value of nonparametric regressionmethods * Statistical techniques accompanied by clear numerical examplesthat further assist readers in developing and implementing theirown solutions * Mathematical equations that are accompanied by a clearexplanation of how the equation was derived The first chapter leads with a compelling argument for studyingnonparametric regression and sets the stage for more advanceddiscussions. In addition to covering standard topics, such askernel and spline methods, the book provides in-depth coverage ofthe smoothing of histograms, a topic generally not covered incomparable texts. With a learning-by-doing approach, each topical chapter includesthorough S-Plus? examples that allow readers to duplicate the sameresults described in the chapter. A separate appendix is devoted tothe conversion of S-Plus objects to R objects. In addition, eachchapter ends with a set of problems that test readers' grasp of keyconcepts and techniques and also prepares them for more advancedtopics. This book is recommended as a textbook for undergraduate andgraduate courses in nonparametric regression. Only a basicknowledge of linear algebra and statistics is required. Inaddition, this is an excellent resource for researchers andengineers in such fields as pattern recognition, speechunderstanding, and data mining. Practitioners who rely onnonparametric regression for analyzing data in the physical,biological, and social sciences, as well as in finance andeconomics, will find this an unparalleled resource.

The Analysis Of Covariance And Alternatives

Author: Bradley Huitema
Publisher: John Wiley & Sons
ISBN: 9781118067468
Size: 39.83 MB
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A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.