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E-raamat: Hierarchical Modeling and Analysis for Spatial Data

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(University of California, Los Angeles, USA), (Duke University, Durham, North Carolina, USA), (University of Minnesota, Minneapolis, USA)
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Like the first edition, this book provides thorough information about the theory and application of hierarchical modeling for spatial and spatiotemporal data. However, the second edition has been completely redone and expanded, it is almost twice the size of the first edition, to reflect the substantially changed knowledge base. New sections include important information on spatial point patterns, ways to deal with big data, theoretical aspects of geostatistical modeling and much more. Also included are more exercises and full color figures to help explore complex topics. Careful attention is paid to new computational software, including WinBugs, spBayes and R packages. Real world applications in environmental science, forestry, public health and real estate make this the essential book on hierarchal modeling for spatial data. Annotation ©2015 Ringgold, Inc., Portland, OR (protoview.com)

Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling

Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application.

New to the Second Edition

  • New chapter on spatial point patterns developed primarily from a modeling perspective
  • New chapter on big data that shows how the predictive process handles reasonably large datasets
  • New chapter on spatial and spatiotemporal gradient modeling that incorporates recent developments in spatial boundary analysis and wombling
  • New chapter on the theoretical aspects of geostatistical (point-referenced) modeling
  • Greatly expanded chapters on methods for multivariate and spatiotemporal modeling
  • New special topics sections on data fusion/assimilation and spatial analysis for data on extremes
  • Double the number of exercises
  • Many more color figures integrated throughout the text
  • Updated computational aspects, including the latest version of WinBUGS, the new flexible spBayes software, and assorted R packages

The Only Comprehensive Treatment of the Theory, Methods, and Software

This second edition continues to provide a complete treatment of the theory, methods, and application of hierarchical modeling for spatial and spatiotemporal data. It tackles current challenges in handling this type of data, with increased emphasis on observational data, big data, and the upsurge of associated software tools. The authors also explore important application domains, including environmental science, forestry, public health, and real estate.

Arvustused

"The second edition of Hierarchical Modeling and Analysis for Spatial Data is a nice, rich, and excellent book, which deserves to be read by students and researchers, especially those working in the area of geosciences, environmental sciences, public health, ecology, and epidemiology. This second edition builds on the strengths of the first edition and includes significant new chapters that make the book rather comprehensive. About 20 different applications are presented along the text (some of them are treated in several chapters). They nicely illustrate the use of the methods that are exposed in the book. These applications are based on data in ecology (), epidemiology and public health (), environmental sciences (), and economics To conclude, the second edition of Hierarchical Modeling and Analysis for Spatial Data provides an excellent treatment of methods and applications in spatial statistics. It takes into consideration 10 years of changes (with respect to the first edition), including the changes induced by the increasing complexity and volume of data and the increasing complexity of questions that one aims to address with modeling and inference approaches. In Chapter 1, the authors claim that they aimed "to present a practical, self-contained treatment of hierarchical modeling, and data analysis for complex spatial (and spatiotemporal) datasets". They succeeded." Samuel Soubeyrand, INRA, France, in Mathematical Geosciences, January 2017

"If you want a thorough taste of the spatial statistics field, Hierarchical Modeling and Analysis for Spatial Data is definitely a book for you. It is accessible and comprehensive, and it fully explores how useful spatial statistics can be without sacrificing the theory it is grounded in. This is a great book for graduate students and professors who want to understand the theoretical underpinnings of the field as well as practitioners who want a toolkit for tackling spatial problems. the authors provide an easy-to-use online resource with all of the books code and datasets. Indeed, these examples are so comprehensive that readers could learn a lot by simply going through them. The accessible theoretical material paired with these detailed examples make Hierarchical Modeling and Analysis for Spatial Data an especially substantial and worthwhile investment. the authors do not hold back on their references. Some could view this book as a spatial statistics biography from the 1950s onward If a graduate student or professor wants a full taste of the spatial statistics literaturewhere it has been, where it is, and where it still needs to gothis is probably one of the best books they could pick up. We find this book to give a much more applied perspective with better computational tools, and thus believe it to be more accessible to a wider audience [ than Cressie]. We recommend this book to anyone who seriously wants to start being involved in spatial statistics." Journal of the American Statistical Association, December 2015

"This is a very welcome second edition of a nice and very successful book written by three experts in the field I have no doubts that this updated text will continue being a compulsory reference for those graduate students and researchers interested in understanding and applying any of the three areas of spatial statistics printed in color and this helps to see better some of the graphical representations excellent book that I highly recommend for anyone interested in the fascinating field of space and spacetime modeling. This is definitely one of those second edition books that is worthwhile having. Many thanks to the authors for their effort." Biometrics, March 2015

Praise for the First Edition:"This book was a pleasure to review. Most of the emphasis is on insight and intuition with relatively little on traditional multivariate techniques. I also found some of the explanations delightful while they did not convert me to Bayesianism, [ the authors] made me reconsider some of my assumptions. They later state 'Our book is intended as a research monograph, presenting the state of the art' and my impression is that they have succeeded In many sections the formulae are augmented by showing R or S code, making it easy to actually apply the mathematics. In summary, this is a nice book." ISI Short Book Reviews

"The book contains a wealth of material not available elsewhere in a unified manner. Each chapter contains worked out examples using some well-known software packages and has exercises with related computer code and data on a supporting web page. The book is up to date in its coverage an important addition to the literature on spatial data analysis." Zentralblatt MATH 1053 "The second edition of Hierarchical Modeling and Analysis for Spatial Data is a nice, rich, and excellent book, which deserves to be read by students and researchers, especially those working in the area of geosciences, environmental sciences, public health, ecology, and epidemiology. This second edition builds on the strengths of the first edition and includes significant new chapters that make the book rather comprehensive. About 20 different applications are presented along the text (some of them are treated in several chapters). They nicely illustrate the use of the methods that are exposed in the book. These applications are based on data in ecology (), epidemiology and public health (), environmental sciences (), and economics To conclude, the second edition of Hierarchical Modeling and Analysis for Spatial Data provides an excellent treatment of methods and applications in spatial statistics. It takes into consideration 10 years of changes (with respect to the first edition), including the changes induced by the increasing complexity and volume of data and the increasing complexity of questions that one aims to address with modeling and inference approaches. In Chapter 1, the authors claim that they aimed "to present a practical, self-contained treatment of hierarchical modeling, and data analysis for complex spatial (and spatiotemporal) datasets". They succeeded." Samuel Soubeyrand, INRA, France, in Mathematical Geosciences, January 2017

"If you want a thorough taste of the spatial statistics field, Hierarchical Modeling and Analysis for Spatial Data is definitely a book for you. It is accessible and comprehensive, and it fully explores how useful spatial statistics can be without sacrificing the theory it is grounded in. This is a great book for graduate students and professors who want to understand the theoretical underpinnings of the field as well as practitioners who want a toolkit for tackling spatial problems. the authors provide an easy-to-use online resource with all of the books code and datasets. Indeed, these examples are so comprehensive that readers could learn a lot by simply going through them. The accessible theoretical material paired with these detailed examples make Hierarchical Modeling and Analysis for Spatial Data an especially substantial and worthwhile investment. the authors do not hold back on their references. Some could view this book as a spatial statistics biography from the 1950s onward If a graduate student or professor wants a full taste of the spatial statistics literaturewhere it has been, where it is, and where it still needs to gothis is probably one of the best books they could pick up. We find this book to give a much more applied perspective with better computational tools, and thus believe it to be more accessible to a wider audience [ than Cressie]. We recommend this book to anyone who seriously wants to start being involved in spatial statistics." Journal of the American Statistical Association, December 2015

"This is a very welcome second edition of a nice and very successful book written by three experts in the field I have no doubts that this updated text will continue being a compulsory reference for those graduate students and researchers interested in understanding and applying any of the three areas of spatial statistics printed in color and this helps to see better some of the graphical representations excellent book that I highly recommend for anyone interested in the fascinating field of space and spacetime modeling. This is definitely one of those second edition books that is worthwhile having. Many thanks to the authors for their effort." Biometrics, March 2015

Praise for the First Edition:"This book was a pleasure to review. Most of the emphasis is on insight and intuition with relatively little on traditional multivariate techniques. I also found some of the explanations delightful while they did not convert me to Bayesianism, [ the authors] made me reconsider some of my assumptions. They later state 'Our book is intended as a research monograph, presenting the state of the art' and my impression is that they have succeeded In many sections the formulae are augmented by showing R or S code, making it easy to actually apply the mathematics. In summary, this is a nice book." ISI Short Book Reviews

"The book contains a wealth of material not available elsewhere in a unified manner. Each chapter contains worked out examples using some well-known software packages and has exercises with related computer code and data on a supporting web page. The book is up to date in its coverage an important addition to the literature on spatial data analysis." Zentralblatt MATH 1053

Preface to the Second Edition xvii
Preface to the First Edition xix
1 Overview of spatial data problems 1(22)
1.1 Introduction to spatial data and models
1(7)
1.1.1 Point-level models
4(1)
1.1.2 Areal models
5(1)
1.1.3 Point process models
6(1)
1.1.4 Software and datasets
7(1)
1.2 Fundamentals of cartography
8(8)
1.2.1 Map projections
8(5)
1.2.2 Calculating distances on the earth's surface
13(3)
1.3 Maps and geodesics in R
16(3)
1.4 Exercises
19(4)
2 Basics of point-referenced data models 23(30)
2.1 Elements of point-referenced modeling
23(8)
2.1.1 Stationarity
23(1)
2.1.2 Variograms
24(1)
2.1.3 Isotropy
25(5)
2.1.4 Variogram model fitting
30(1)
2.2 Anisotropy
31(1)
2.2.1 Geometric anisotropy
31(1)
2.2.2 Other notions of anisotropy
32(1)
2.3 Exploratory approaches for point-referenced data
32(8)
2.3.1 Basic techniques
32(5)
2.3.2 Assessing anisotropy
37(7)
2.3.2.1 Directional semivariograms and rose diagrams
37(1)
2.3.2.2 Empirical semivariogram contour (ESC) plots
38(2)
2.4 Classical spatial prediction
40(4)
2.4.0.3 Noiseless kriging
43(1)
2.5 Computer tutorials
44(6)
2.5.1 EDA and spatial data visualization in R
44(3)
2.5.2 Variogram analysis in R
47(3)
2.6 Exercises
50(3)
3 Some theory for point-referenced data models 53(20)
3.1 Formal modeling theory for spatial processes
53(10)
3.1.1 Some basic stochastic process theory for spatial processes
55(2)
3.1.2 Covariance functions and spectra
57(3)
3.1.2.1 More general isotropic correlation functions
60(1)
3.1.3 Constructing valid covariance functions
60(1)
3.1.4 Smoothness of process realizations
61(2)
3.1.5 Directional derivative processes
63(1)
3.2 Nonstationary spatial process models *
63(7)
3.2.1 Deformation
64(1)
3.2.2 Nonstationarity through kernel mixing of process variables
65(4)
3.2.3 Mixing of process distributions
69(1)
3.3 Exercises
70(3)
4 Basics of areal data models 73(24)
4.1 Exploratory approaches for areal data
74(4)
4.1.1 Measures of spatial association
75(2)
4.1.2 Spatial smoothers
77(1)
4.2 Brook's Lemma and Markov random fields
78(2)
4.3 Conditionally autoregressive (CAR) models
80(5)
4.3.1 The Gaussian case
81(3)
4.3.2 The non-Gaussian case
84(1)
4.4 Simultaneous autoregressive (SAR) models
85(3)
4.4.1 CAR versus SAR models
87(1)
4.4.2 STAR models
87(1)
4.5 Computer tutorials
88(7)
4.5.1 Adjacency matrices from maps using spdep
89(1)
4.5.2 Moran's I and Geary's C in spdep
90(1)
4.5.3 SAR and CAR model fitting using spdep in R
90(5)
4.6 Exercises
95(2)
5 Basics of Bayesian inference 97(26)
5.1 Introduction to hierarchical modeling and Bayes' Theorem
97(3)
5.1.1 Illustrations of Bayes' Theorem
98(2)
5.2 Bayesian inference
100(7)
5.2.1 Point estimation
100(1)
5.2.2 Interval estimation
101(1)
5.2.3 Hypothesis testing and model choice
101(6)
5.2.3.1 Bayes factors
102(1)
5.2.3.2 The DIC criterion
103(2)
5.2.3.3 Posterior predictive loss criterion
105(1)
5.2.3.4 Model assessment using hold out data
106(1)
5.3 Bayesian computation
107(8)
5.3.1 The Gibbs sampler
108(1)
5.3.2 The Metropolis-Hastings algorithm
109(2)
5.3.3 Slice sampling
111(1)
5.3.4 Convergence diagnosis
112(1)
5.3.5 Variance estimation
113(2)
5.4 Computer tutorials
115(3)
5.4.1 Basic Bayesian modeling in R
115(1)
5.4.2 Advanced Bayesian modeling in WinBUGS
116(2)
5.5 Exercises
118(5)
6 Hierarchical modeling for univariate spatial data 123(42)
6.1 Stationary spatial process models
123(13)
6.1.1 Isotropic models
124(3)
6.1.1.1 Prior specification
124(3)
6.1.2 Bayesian kriging in WinBUGS
127(2)
6.1.3 More general isotropic correlation functions, revisited
129(4)
6.1.4 Modeling geometric anisotropy
133(3)
6.2 Generalized linear spatial process modeling
136(3)
6.3 Fitting hierarchical models for point-referenced data in spBayes
139(11)
6.3.1 Gaussian spatial regression models
139(7)
6.3.1.1 Prediction
143(2)
6.3.1.2 Model selection
145(1)
6.3.2 Non-Gaussian spatial GLM
146(4)
6.4 Areal data models
150(10)
6.4.1 Disease mapping
150(1)
6.4.2 Traditional models and frequentist methods
151(1)
6.4.3 Hierarchical Bayesian methods
152(7)
6.4.3.1 Poisson-gamma model
152(1)
6.4.3.2 Poisson-lognormal models
153(2)
6.4.3.3 CAR models and their difficulties
155(4)
6.4.4 Extending the CAR model
159(1)
6.5 General linear areal data modeling
160(1)
6.6 Comparison of point-referenced and areal data models
160(1)
6.7 Exercises
161(4)
7 Spatial misalignment 165(34)
7.1 Point-level modeling
166(7)
7.1.1 Gaussian process models
166(2)
7.1.1.1 Motivating data set
166(1)
7.1.1.2 Model assumptions and analytic goals
167(1)
7.1.2 Methodology for the point-level realignment
168(5)
7.2 Nested block-level modeling
173(6)
7.2.1 Methodology for nested block-level realignment
173(4)
7.2.2 Individual block group estimation
177(2)
7.2.3 Aggregate estimation: Block groups near the Ithaca, NY, waste site
179(1)
7.3 Nonnested block-level modeling
179(10)
7.3.1 Motivating data set
180(2)
7.3.2 Methodology for nonnested block-level realignment
182(21)
7.3.2.1 Total population interpolation model
186(2)
7.3.2.2 Age and sex effects
188(1)
7.4 A data assimilation example
189(1)
7.5 Misaligned regression modeling
190(5)
7.6 Exercises
195(4)
8 Modeling and Analysis for Point Patterns 199(58)
8.1 Introduction
199(4)
8.2 Theoretical development
203(4)
8.2.1 Counting measure
204(1)
8.2.2 Moment measures
205(2)
8.3 Diagnostic tools
207(6)
8.3.1 Exploratory data analysis; investigating complete spatial randomness
208(1)
8.3.2 G and F functions
208(2)
8.3.3 The K function
210(3)
8.3.4 Empirical estimates of the intensity
213(1)
8.4 Modeling point patterns; NHPP's and Cox processes
213(7)
8.4.1 Parametric specifications
215(1)
8.4.2 Nonparametric specifications
216(1)
8.4.3 Bayesian modeling details
217(1)
8.4.3.1 The "poor man's" version; revisiting the ecological fallacy
218(1)
8.4.4 Examples
218(2)
8.5 Generating point patterns
220(1)
8.6 More general point pattern models
221(7)
8.6.1 Cluster processes
221(2)
8.6.1.1 Neyman-Scott processes
222(1)
8.6.2 Shot noise processes
223(1)
8.6.3 Gibbs processes
224(1)
8.6.4 Further Bayesian model fitting and inference
225(1)
8.6.5 Implementing fully Bayesian inference
226(1)
8.6.6 An example
226(2)
8.7 Marked point processes
228(9)
8.7.1 Model specifications
228(1)
8.7.2 Bayesian model fitting for marked point processes
229(1)
8.7.3 Modeling clarification
230(1)
8.7.4 Enriching intensities
231(6)
8.7.4.1 Introducing non-spatial covariate information
233(2)
8.7.4.2 Results of the analysis
235(2)
8.8 Space-time point patterns
237(5)
8.8.1 Space-time Poisson process models
238(1)
8.8.2 Dynamic models for discrete time data
238(1)
8.8.3 Space-time Cox process models using stochastic PDE's
239(3)
8.8.3.1 Modeling the house construction data for Irving, TX
240(2)
8.8.3.2 Results of the data analysis
242(1)
8.9 Additional topics
242(11)
8.9.1 Measurement error in point patterns
242(4)
8.9.1.1 Modeling details
244(2)
8.9.2 Presence-only data application
246(5)
8.9.2.1 Probability model for presence locations
247(4)
8.9.3 Scan statistics
251(1)
8.9.4 Preferential sampling
252(1)
8.10 Exercises
253(4)
9 Multivariate spatial modeling for point-referenced data 257(48)
9.1 Joint modeling in classical multivariate geostatistics
257(4)
9.1.1 Co-kriging
259(1)
9.1.2 Intrinsic multivariate correlation and nested models
260(1)
9.2 Some theory for cross-covariance functions
261(2)
9.3 Separable models
263(1)
9.4 Spatial prediction, interpolation, and regression
264(14)
9.4.1 Regression in the Gaussian case
266(2)
9.4.2 Avoiding the symmetry of the cross-covariance matrix
268(1)
9.4.3 Regression in a probit model
268(1)
9.4.4 Examples
269(3)
9.4.5 Conditional modeling
272(3)
9.4.6 Spatial regression with kernel averaged predictors
275(3)
9.5 Coregionalization models
278(5)
9.5.1 Coregionalization models and their properties
278(3)
9.5.2 Unconditional and conditional Bayesian specifications
281(2)
9.5.2.1 Equivalence of likelihoods
281(1)
9.5.2.2 Equivalence of prior specifications
282(1)
9.6 Spatially varying coefficient models
283(10)
9.6.1 Approach for a single covariate
285(1)
9.6.2 Multivariate spatially varying coefficient models
286(2)
9.6.3 Spatially varying coregionalization models
288(1)
9.6.4 Model-fitting issues
288(5)
9.6.4.1 Fitting the joint model
288(1)
9.6.4.2 Fitting the conditional model
289(4)
9.7 Other constructive approaches
293(4)
9.7.1 Generalized linear model setting
296(1)
9.8 Illustrating multivariate spatial modeling with spBayes
297(4)
9.9 Exercises
301(4)
10 Models for multivariate areal data 305(24)
10.1 The multivariate CAR (MCAR) distribution
306(2)
10.2 Modeling with a proper, non-separable MCAR distribution
308(3)
10.3 Conditionally specified Generalized MCAR (GMCAR) distributions
311(3)
10.4 Modeling using the GMCAR distribution
314(1)
10.5 Illustration: Fitting conditional GMCAR to Minnesota cancer data
315(4)
10.6 Coregionalized MCAR distributions
319(3)
10.6.1 Case 1: Independent and identical latent CAR variables
319(1)
10.6.2 Case 2: Independent but not identical latent CAR variables
320(1)
10.6.3 Case 3: Dependent and not identical latent CAR variables
321(1)
10.7 Modeling with coregionalized MCAR's
322(2)
10.8 Illustrating coregionalized MCAR models with three cancers from Minnesota
324(3)
10.9 Exercises
327(2)
11 Spatiotemporal modeling 329(52)
11.1 General modeling formulation
330(9)
11.1.1 Preliminary analysis
330(1)
11.1.2 Model formulation
331(2)
11.1.3 Associated distributional results
333(2)
11.1.4 Prediction and forecasting
335(4)
11.2 Point-level modeling with continuous time
339(4)
11.3 Nonseparable spatiotemporal models
343(1)
11.4 Dynamic spatiotemporal models
344(8)
11.4.1 Brief review of dynamic linear models
345(1)
11.4.2 Formulation for spatiotemporal models
345(3)
11.4.3 Spatiotemporal data
348(4)
11.5 Fitting dynamic spatiotemporal models using spBayes
352(3)
11.6 Geostatistical space-time modeling driven by differential equations
355(6)
11.7 Areal unit space-time modeling
361(12)
11.7.1 Aligned data
361(4)
11.7.2 Misalignment across years
365(2)
11.7.3 Nested misalignment both within and across years
367(3)
11.7.4 Nonnested misalignment and regression
370(3)
11.8 Areal-level continuous time modeling
373(5)
11.8.1 Areally referenced temporal processes
374(2)
11.8.2 Hierarchical modeling
376(2)
11.9 Exercises
378(3)
12 Modeling large spatial and spatiotemporal datasets 381(32)
12.1 Introduction
381(1)
12.2 Appr4mate likelihood approaches
381(5)
12.2.1 Spectral methods
381(1)
12.2.2 Lattice and conditional independence methods
382(1)
12.2.3 INLA
383(1)
12.2.4 Approximate likelihood
384(1)
12.2.5 Variational Bayes algorithm for spatial models
384(2)
12.2.6 Covariance tapering
386(1)
12.3 Models for large spatial data: low rank models
386(4)
12.3.1 Kernel-based dimension reduction
387(1)
12.3.2 The Karhunen-Loeve representation of Gaussian processes
388(2)
12.4 Predictive process models
390(10)
12.4.1 The predictive process
390(2)
12.4.2 Properties of the predictive process
392(1)
12.4.3 Biases in low-rank models and the bias-adjusted modified predictive process
393(2)
12.4.4 Selection of knots
395(2)
12.4.5 A simulation example using the two step analysis
397(1)
12.4.6 Non-Gaussian first stage models
397(1)
12.4.7 Spatiotemporal versions
398(1)
12.4.8 Multivariate predictive process models
399(1)
12.5 Modeling with the predictive process
400(4)
12.6 Fitting a predictive process model in spBayes
404(7)
12.7 Exercises
411(2)
13 Spatial gradients and wombling 413(34)
13.1 Introduction
413(2)
13.2 Process smoothness revisited *
415(2)
13.3 Directional finite difference and derivative processes
417(1)
13.4 Distribution theory for finite differences and directional gradients
418(2)
13.5 Directional derivative processes in modeling
420(2)
13.6 Illustration: Inference for differences and gradients
422(2)
13.7 Curvilinear gradients and wombling
424(3)
13.7.1 Gradients along curves
424(2)
13.7.2 Wombling boundary
426(1)
13.8 Distribution theory for curvilinear gradients
427(2)
13.9 Illustration: Spatial boundaries for invasive plant species
429(3)
13.10 Areal wombling
432(13)
13.10.1 Review of existing methods
433(1)
13.10.2 Simple MRF-based areal wombling
434(4)
13.10.2.1 Adding covariates
437(1)
13.10.3 Joint site-edge areal wombling
438(6)
13.10.3.1 Edge smoothing and random neighborhood structure
439(1)
13.10.3.2 Two-level CAR model
439(1)
13.10.3.3 Site-edge (SE) models
440(4)
13.10.4 FDR-based areal wombling
444(1)
13.11 Wombling with point process data
445(1)
13.12 Concluding remarks
445(2)
14 Spatial survival models 447(32)
14.1 Parametric models
448(6)
14.1.1 Univariate spatial frailty modeling
448(5)
14.1.1.1 Bayesian implementation
449(4)
14.1.2 Spatial frailty versus logistic regression models
453(1)
14.2 Semiparametric models
454(3)
14.2.1 Beta mixture approach
455(1)
14.2.2 Counting process approach
456(1)
14.3 Spatiotemporal models
457(5)
14.3.1 Results for the full model
459(1)
14.3.2 Bayesian model choice
460(2)
14.4 Multivariate models
462(4)
14.4.1 Static spatial survival data with multiple causes of death
462(1)
14.4.2 MCAR specification, simplification, and computing
462(1)
14.4.3 Spatiotemporal survival data
463(3)
14.5 Spatial cure rate models
466(9)
14.5.1 Models for right- and interval-censored data
468(3)
14.5.1.1 Right-censored data
468(3)
14.5.1.2 Interval-censored data
471(1)
14.5.2 Spatial frailties in cure rate models
471(1)
14.5.3 Model comparison
472(3)
14.6 Exercises
475(4)
15 Special topics in spatial process modeling 479(22)
15.1 Data assimilation
479(7)
15.1.1 Algorithmic and pseudo-statistical approaches in weather prediction
479(1)
15.1.2 Fusion modeling using stochastic integration
480(2)
15.1.3 The downscaler
482(2)
15.1.4 Spatiotemporal versions
484(1)
15.1.5 An illustration
485(1)
15.2 Space-time modeling for extremes
486(6)
15.2.1 Possibilities for modeling maxima
487(1)
15.2.2 Review of extreme value theory
488(1)
15.2.3 A continuous spatial process model
489(1)
15.2.4 Using copulas
490(1)
15.2.5 Hierarchical modeling for spatial extreme values
491(1)
15.3 Spatial CDF's
492(9)
15.3.1 Basic definitions and motivating data sets
492(3)
15.3.2 Derived-process spatial CDF's
495(1)
15.3.2.1 Point- versus block-level spatial CDF's
495(1)
15.3.2.2 Covariate weighted SCDF's for misaligned data
496(1)
15.3.3 Randomly weighted SCDF's
496(5)
Appendices 501(28)
A Spatial computing methods
503(12)
A.1 Fast Fourier transforms
503(1)
A.2 Slice Gibbs sampling for spatial process model fitting
504(5)
A.2.1 Constant mean process with nugget
507(1)
A.2.2 Mean structure process with no pure error component
508(1)
A.2.3 Mean structure process with nugget
509(1)
A.3 Structured MCMC sampling for areal model fitting
509(4)
A.3.1 SMCMC algorithm basics
510(1)
A.3.2 Applying structured MCMC to areal data
510(2)
A.3.3 Algorithmic schemes
512(1)
A.4 spBayes: Under the hood
513(2)
B Answers to selected exercises
515(14)
Bibliography 529(30)
Index 559
Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
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