| Description |
1 online resource (xxii, 464 pages) : illustrations, maps |
| Series |
Interdisciplinary statistics series |
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Interdisciplinary statistics.
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| Contents |
Intro; Halftitle Page; Title Page; Copyright; Table of Contents; List of Tables; Preface to Third Edition; Preface to Second Edition; Preface to First Edition; I Background; 1 Introduction; 1.1 Data Sets; 2 Bayesian Inference and Modeling; 2.1 Likelihood Models; 2.1.1 Spatial Correlation; 2.1.1.1 Conditional Independence; 2.1.1.2 Joint Densities with Correlations; 2.1.1.3 Pseudolikelihood Approximation; 2.2 Prior Distributions; 2.2.1 Propriety; 2.2.2 Non-Informative Priors; 2.3 Posterior Distributions; 2.3.1 Conjugacy; 2.3.2 Prior Choice; 2.3.2.1 Regression Parameters |
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2.3.2.2 Variance or Precision Parameters2.3.2.3 Correlation Parameters; 2.3.2.4 Probabilities; 2.3.2.5 Correlated Parameters; 2.4 Predictive Distributions; 2.4.1 Poisson-Gamma Example; 2.5 Bayesian Hierarchical Modeling; 2.6 Hierarchical Models; 2.7 Posterior Inference; 2.7.1 Bernoulli and Binomial Examples; 2.8 Exercises; 3 Computational Issues; 3.1 Posterior Sampling; 3.2 Markov Chain Monte Carlo (MCMC) Methods; 3.3 Metropolis and Metropolis-Hastings Algorithms; 3.3.1 Metropolis Updates; 3.3.2 Metropolis-Hastings Updates; 3.3.3 Gibbs Updates |
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3.3.4 Metropolis-Hastings (M-H) versus Gibbs Algorithms3.3.5 Special Methods; 3.3.6 Convergence; 3.3.6.1 Single-Chain Methods; 3.3.6.2 Multi-Chain Methods; 3.3.7 Subsampling and Thinning; 3.3.7.1 Monitoring Metropolis-Like Samplers; 3.4 Perfect Sampling; 3.5 Posterior and Likelihood Approximations; 3.5.1 Pseudolikelihood and Other Forms; 3.5.2 Asymptotic Approximations; 3.5.2.1 Asymptotic Quadratic Form; 3.5.2.2 Laplace Integral Approximation; 3.5.2.3 INLA and R-INLA; 3.6 Alternative Computational Aproaches; 3.6.1 Maximum A Posteriori Estimation (MAP); 3.6.2 Iterated Conditional Modes (ICMs) |
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3.6.3 MC3 and Parallel Tempering3.6.4 Variational Bayes; 3.6.5 Sequential Monte Carlo; 3.7 Approximate Bayesian Computation (ABC); 3.8 Exercises; 4 Residuals and Goodness-of-Fit; 4.1 Model GOF Measures; 4.1.1 Deviance Information Criterion; 4.1.2 Posterior Predictive Loss; 4.2 General Residuals; 4.3 Bayesian Residuals; 4.4 Predictive Residuals and Bootstrap; 4.4.1 Conditional Predictive Ordinates (CPOs.); 4.5 Interpretation of Residuals in a Bayesian Setting; 4.6 Pseudo-Bayes Factors and Marginal Predictive Likelihood; 4.7 Other Diagnostics; 4.8 Exceedance Probabilities; 4.9 Exercises |
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II Themes5 Disease Map Reconstruction and Relative Risk Estimation; 5.1 Introduction to Case Event and Count Likelihoods; 5.1.1 Poisson Process Model; 5.1.2 Conditional Logistic Model; 5.1.3 Binomial Model for Count Data; 5.1.4 Poisson Model for Count Data; 5.1.4.1 Standardisation; 5.1.4.2 Relative Risk; 5.2 Specification of Predictor in Case Event and Count Models; 5.2.1 Bayesian Linear Model; 5.3 Simple Case and Count Data Models with Uncorrelated Random Effects; 5.3.1 Gamma and Beta Models; 5.3.1.1 Gamma Models; 5.3.1.1.1 Hyperprior Distributions; 5.3.1.1.2 Linear Parameterization |
| Summary |
"Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. In addition to the new material, the book also covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. The target audience for this text is public health specialists, epidemiologists, and biostatisticians who need to work with geo-referenced health data."--Provided by publisher |
| Notes |
"A Chapman & Hall book." |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Description based on print version record |
| Subject |
Medical mapping.
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Epidemiology -- Statistical methods
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Bayesian statistical decision theory.
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Bayes Theorem
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Spatial Analysis
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Models, Statistical
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MEDICAL -- Forensic Medicine.
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MEDICAL -- Preventive Medicine.
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MEDICAL -- Public Health.
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Medical mapping
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Epidemiology -- Statistical methods
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Bayesian statistical decision theory
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Epidemiology
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Mathematical statistics
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Mathematical statistics -- Data processing
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| Form |
Electronic book
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| LC no. |
2020691302 |
| ISBN |
9781351271745 |
|
1351271741 |
|
9781351271769 |
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1351271768 |
|
9781351271752 |
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135127175X |
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