| Description |
1 online resource (274 pages) |
| Series |
Chapman and Hall/CRC Interdisciplinary Statistics Ser |
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Interdisciplinary statistics.
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| Contents |
Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; Preface; List of Figures; List of Tables; 1: Introduction; 1.1 Motivating example: mapping river-blindness in Africa; 1.2 Empirical or mechanistic models; 1.3 What is in this book?; 2: Regression modelling for spatially referenced data; 2.1 Linear regression models; 2.1.1 Malnutrition in Ghana; 2.2 Generalised linear models; 2.2.1 Logistic Binomial regression: river-blindness in Liberia; 2.2.2 Log-linear Poisson regression: abundance of Anopheles Gambiae mosquitoes in Southern Cameroon |
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2.3 Questioning the assumption of independence2.3.1 Testing for residual spatial correlation: the empirical variogram; 3: Theory; 3.1 Gaussian processes; 3.2 Families of spatial correlation functions; 3.2.1 The exponential family; 3.2.2 The Matérn family; 3.2.3 The spherical family; 3.2.4 The theoretical variogram and the nugget variance; 3.3 Statistical inference; 3.3.1 Likelihood-based inference; 3.4 Bayesian Inference; 3.5 Predictive inference; 3.6 Approximations to Gaussian processes; 3.6.1 Low-rank approximations |
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3.6.2 Gaussian Markov random field approximations via stochastic partial differential equations4: The linear geostatistical model; 4.1 Model formulation; 4.2 Inference; 4.2.1 Likelihood-based inference; 4.2.1.1 Maximum likelihood estimation; 4.2.2 Bayesian inference; 4.2.3 Trans-Gaussian models; 4.3 Model validation; 4.3.1 Scenario 1: omission of the nugget effect; 4.3.2 Scenario 2: miss-specification of the smoothness parameter; 4.3.3 Scenario 3: non-Gaussian data; 4.4 Spatial prediction; 4.5 Applications; 4.5.1 Heavy metal monitoring in Galicia; 4.5.2 Malnutrition in Ghana (continued) |
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4.5.2.1 Spatial predictions for the target population5: Generalised linear geostatistical models; 5.1 Model formulation; 5.1.1 Binomial sampling; 5.1.2 Poisson sampling; 5.1.3 Negative binomial sampling?; 5.2 Inference; 5.2.1 Likelihood-based inference; 5.2.1.1 Laplace approximation; 5.2.1.2 Monte Carlo maximum likelihood; 5.2.2 Bayesian inference; 5.3 Model validation; 5.4 Spatial prediction; 5.5 Applications; 5.5.1 River-blindness in Liberia (continued); 5.5.2 Abundance of Anopheles Gambiae mosquitoes in Southern Cameroon (continued) |
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5.6 A link between geostatistical models and point processes5.7 A link between geostatistical models and spatially discrete processes; 6: Geostatistical design; 6.1 Introduction; 6.2 Definitions; 6.3 Non-adaptive designs; 6.3.1 Two extremes: completely random and completely regular designs; 6.3.2 Inhibitory designs; 6.3.3 Inhibitory-plus-close-pairs designs; 6.3.3.1 Comparing designs: a simple example; 6.3.4 Modified regular lattice designs; 6.3.5 Application: rolling malaria indicator survey sampling in the Majete perimeter, southern Malawi; 6.4 Adaptive designs |
| Notes |
6.4.1 An adaptive design algorithm |
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Print version record |
| Subject |
Public health -- Research -- Statistical methods
|
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Geology -- Statistical methods.
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Geology -- Statistical methods.
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Public health -- Research -- Statistical methods.
|
| Form |
Electronic book
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| Author |
Giorgi, Emanuele.
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| ISBN |
9781351743273 |
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1351743279 |
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