{"product_id":"spatial-and-spatiotemporal-geostatistical-modeling-and-kriging-9781118413180","title":"Spatial and SpatioTemporal Geostatistical","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eStatistical 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.\u003c\/p\u003e \u003cp\u003eThis book includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eMethods for selecting valid covariance functions from the empirical counterparts that overcome the existing limitations of the traditional methods.\u003c\/li\u003e \u003cli\u003eThe most innovative developments in the different steps of the kriging process.\u003c\/li\u003e \u003cli\u003eAn up-to-date account of strategies for dealing with data evolving in space and time.\u003c\/li\u003e \u003cli\u003eAn accompanying website featuring R code and examples\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eList of figures xi\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eList of tables xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eForeword xix\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface xxi\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eThe companion website xxiii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 From classical statistics to geostatistics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Not all spatial data are geostatistical data 1\u003c\/p\u003e \u003cp\u003e1.2 The limits of classical statistics 5\u003c\/p\u003e \u003cp\u003e1.3 A real geostatistical dataset: data on carbon monoxide in Madrid, Spain 7\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Geostatistics: preliminaries 10\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Regionalized variables 10\u003c\/p\u003e \u003cp\u003e2.2 Random functions 11\u003c\/p\u003e \u003cp\u003e2.3 Stationary and intrinsic hypotheses 13\u003c\/p\u003e \u003cp\u003e2.3.1 Stationarity 13\u003c\/p\u003e \u003cp\u003e2.3.2 Stationary random functions in the strict sense 14\u003c\/p\u003e \u003cp\u003e2.3.3 Second-order stationary random functions 15\u003c\/p\u003e \u003cp\u003e2.3.4 Intrinsically stationary random functions 16\u003c\/p\u003e \u003cp\u003e2.3.5 Non-stationary random functions 18\u003c\/p\u003e \u003cp\u003e2.4 Support 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Structural analysis 20\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 20\u003c\/p\u003e \u003cp\u003e3.2 Covariance function 21\u003c\/p\u003e \u003cp\u003e3.2.1 Definition and properties 21\u003c\/p\u003e \u003cp\u003e3.2.2 Some theoretical isotropic covariance functions 23\u003c\/p\u003e \u003cp\u003e3.3 Empirical covariogram 26\u003c\/p\u003e \u003cp\u003e3.4 Semivariogram 27\u003c\/p\u003e \u003cp\u003e3.4.1 Definition and properties 27\u003c\/p\u003e \u003cp\u003e3.4.2 Behavior at intermediate and large distances 30\u003c\/p\u003e \u003cp\u003e3.4.3 Behavior near the origin 31\u003c\/p\u003e \u003cp\u003e3.4.4 A discontinuity at the origin 33\u003c\/p\u003e \u003cp\u003e3.5 Theoretical semivariogram models 35\u003c\/p\u003e \u003cp\u003e3.5.1 Semivariograms with a sill 36\u003c\/p\u003e \u003cp\u003e3.5.2 Semivariograms with a hole effect 46\u003c\/p\u003e \u003cp\u003e3.5.3 Semivariograms without a sill 47\u003c\/p\u003e \u003cp\u003e3.5.4 Combining semivariogram models 50\u003c\/p\u003e \u003cp\u003e3.6 Empirical semivariogram 52\u003c\/p\u003e \u003cp\u003e3.7 Anisotropy 64\u003c\/p\u003e \u003cp\u003e3.8 Fitting a semivariogram model 69\u003c\/p\u003e \u003cp\u003e3.8.1 Manual fitting 70\u003c\/p\u003e \u003cp\u003e3.8.2 Automatic fitting 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Spatial prediction and kriging 80\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 80\u003c\/p\u003e \u003cp\u003e4.2 Neighborhood 83\u003c\/p\u003e \u003cp\u003e4.3 Ordinary kriging 84\u003c\/p\u003e \u003cp\u003e4.3.1 Point observation support and point predictor 84\u003c\/p\u003e \u003cp\u003e4.3.2 Effects of a change in the model parameters 90\u003c\/p\u003e \u003cp\u003e4.3.3 Point observation support and block predictor 99\u003c\/p\u003e \u003cp\u003e4.3.4 Block observation support and block predictor 110\u003c\/p\u003e \u003cp\u003e4.4 Simple kriging: the special case of known mean 113\u003c\/p\u003e \u003cp\u003e4.5 Simple kriging with an estimated mean 115\u003c\/p\u003e \u003cp\u003e4.6 Universal kriging 116\u003c\/p\u003e \u003cp\u003e4.6.1 Point observation support and point predictor 116\u003c\/p\u003e \u003cp\u003e4.6.2 Point observation support and block predictor 121\u003c\/p\u003e \u003cp\u003e4.6.3 Block observation support and block predictor 121\u003c\/p\u003e \u003cp\u003e4.6.4 Kriging and exact interpolation 122\u003c\/p\u003e \u003cp\u003e4.7 Residual kriging 122\u003c\/p\u003e \u003cp\u003e4.7.1 Direct residual kriging 123\u003c\/p\u003e \u003cp\u003e4.7.2 Iterative residual kriging 124\u003c\/p\u003e \u003cp\u003e4.7.3 Modified iterative residual kriging 125\u003c\/p\u003e \u003cp\u003e4.8 Median-Polish kriging 125\u003c\/p\u003e \u003cp\u003e4.9 Cross-validation 134\u003c\/p\u003e \u003cp\u003e4.10 Non-linear kriging 138\u003c\/p\u003e \u003cp\u003e4.10.1 Disjunctive kriging 138\u003c\/p\u003e \u003cp\u003e4.10.2 Indicator kriging 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Geostatistics and spatio-temporal random functions 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Spatio-temporal geostatistics 145\u003c\/p\u003e \u003cp\u003e5.2 Spatio-temporal continuity 146\u003c\/p\u003e \u003cp\u003e5.3 Relevant spatio-temporal concepts 147\u003c\/p\u003e \u003cp\u003e5.4 Properties of the spatio-temporal covariance and semivariogram 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Spatio-temporal structural analysis (I): empirical semivariogram\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eand covariogram estimation and model fitting 162\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 162\u003c\/p\u003e \u003cp\u003e6.2 The empirical spatio-temporal semivariogram and covariogram 163\u003c\/p\u003e \u003cp\u003e6.3 Fitting spatio-temporal semivariogram and covariogram models 170\u003c\/p\u003e \u003cp\u003e6.4 Validation and comparison of spatio-temporal semivariogram and covariogram models 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Spatio-temporal structural analysis (II): theoretical covariance models 178\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 178\u003c\/p\u003e \u003cp\u003e7.2 Combined distance or metric model 180\u003c\/p\u003e \u003cp\u003e7.3 Sum model 183\u003c\/p\u003e \u003cp\u003e7.4 Combined metric-sum model 184\u003c\/p\u003e \u003cp\u003e7.5 Product model 187\u003c\/p\u003e \u003cp\u003e7.6 Product-sum model 191\u003c\/p\u003e \u003cp\u003e7.7 Porcu and Mateu mixture-based models 192\u003c\/p\u003e \u003cp\u003e7.8 General product-sum model 194\u003c\/p\u003e \u003cp\u003e7.9 Integrated product and product-sum models 198\u003c\/p\u003e \u003cp\u003e7.10 Models proposed by Cressie and Huang 201\u003c\/p\u003e \u003cp\u003e7.11 Models proposed by Gneiting 207\u003c\/p\u003e \u003cp\u003e7.12 Mixture models proposed by Ma 211\u003c\/p\u003e \u003cp\u003e7.12.1 Covariance functions generated by scale mixtures 211\u003c\/p\u003e \u003cp\u003e7.12.2 Covariance functions generated by positive power mixtures 212\u003c\/p\u003e \u003cp\u003e7.13 Models generated by linear combinations proposed by Ma 215\u003c\/p\u003e \u003cp\u003e7.14 Models proposed by Stein 222\u003c\/p\u003e \u003cp\u003e7.15 Construction of covariance functions using copulas and completely monotonic functions 223\u003c\/p\u003e \u003cp\u003e7.16 Generalized product-sum model 223\u003c\/p\u003e \u003cp\u003e7.17 Models that are not fully symmetric 236\u003c\/p\u003e \u003cp\u003e7.18 Mixture-based Bernstein zonally anisotropic covariance functions 237\u003c\/p\u003e \u003cp\u003e7.19 Non-stationary models 241\u003c\/p\u003e \u003cp\u003e7.19.1 Mixture of locally orthogonal stationary processes 241\u003c\/p\u003e \u003cp\u003e7.19.2 Non-stationary models proposed by Ma 242\u003c\/p\u003e \u003cp\u003e7.19.3 Non-stationary models proposed by Porcu and Mateu 246\u003c\/p\u003e \u003cp\u003e7.20 Anisotropic covariance functions by Porcu and Mateu 247\u003c\/p\u003e \u003cp\u003e7.20.1 Constructing temporally symmetric and spatially anisotropic covariance functions 247\u003c\/p\u003e \u003cp\u003e7.20.2 Generalizing the class of spatio-temporal covariance functions proposed by Gneiting 248\u003c\/p\u003e \u003cp\u003e7.20.3 Differentiation and integration operators acting on classes of anisotropic covariance functions on the basis of isotropic components: ‘La descente étendue’ 251\u003c\/p\u003e \u003cp\u003e7.21 Spatio-temporal constructions based on quasi-arithmetic means of covariance functions 253\u003c\/p\u003e \u003cp\u003e7.21.1 Multivariate quasi-arithmetic compositions 255\u003c\/p\u003e \u003cp\u003e7.21.2 Permissibility criteria for quasi-arithmetic means of covariance functions in ℝ\u003ci\u003ed \u003c\/i\u003e256\u003c\/p\u003e \u003cp\u003e7.21.3 The use of quasi-arithmetic functionals to build non-separable, stationary, spatio-temporal covariance functions 259\u003c\/p\u003e \u003cp\u003e7.21.4 Quasi-arithmeticity and non-stationarity in space 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Spatio-temporal prediction and kriging 266\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Spatio-temporal kriging 266\u003c\/p\u003e \u003cp\u003e8.2 Spatio-temporal kriging equations 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 An introduction to functional geostatistics 274\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Functional data analysis 274\u003c\/p\u003e \u003cp\u003e9.2 Functional geostatistics: The parametric vs. the non-parametric approach 279\u003c\/p\u003e \u003cp\u003e9.3 Functional ordinary kriging 283\u003c\/p\u003e \u003cp\u003e9.3.1 Preliminaries 283\u003c\/p\u003e \u003cp\u003e9.3.2 Functional ordinary kriging equations 284\u003c\/p\u003e \u003cp\u003e9.3.3 Estimating the trace-semivariogram 288\u003c\/p\u003e \u003cp\u003e9.3.4 Functional cross-validation 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Spectral representations 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Probabilistic aspects of \u003c\/b\u003e\u003cb\u003e\u003ci\u003eU\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e\u003ci\u003eij \u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e= \u003c\/b\u003e\u003cb\u003e\u003ci\u003eZ\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e(\u003c\/b\u003e\u003cb\u003e\u003ci\u003es\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e\u003ci\u003ei\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e)\u003c\/b\u003e−\u003cb\u003e\u003ci\u003eZ\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e(\u003c\/b\u003e\u003cb\u003e\u003ci\u003es\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e\u003ci\u003ej\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e\u003ci\u003e) \u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e300\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Basic theory on restricted maximum likelihood 302\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Most relevant proofs 304\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography and further reading 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 351\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406866391383,"sku":"9781118413180","price":63.86,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/spatial-and-spatiotemporal-geostatistical-modeling-and-kriging-9781118413180","provider":"Book Curl","version":"1.0","type":"link"}