Spatio-temporal statistics with R. ([2019])

• Record Type: Book
• Further Information: Note: Christopher K. Wikle, Andrew Zammit-Mangion, Noel Cressie.
• Authors: Wikle, Christopher K, 1963-; Zammit-Mangion, Andrew; Cressie, Noel A. C
• Contents: Cover; Half Title; Series Page; Title Page; Copyright Page; Contents; Acknowledgements; Preface; 1 Introduction to Spatio-Temporal Statistics; 1.1 Why Should Spatio-Temporal Models Be Statistical?; 1.2 Goals of Spatio-Temporal Statistics; 1.2.1 The Two Ds of Spatio-Temporal Statistical Modeling; 1.2.2 Descriptive Modeling; 1.2.3 Dynamic Modeling; 1.3 Hierarchical Statistical Models; 1.4 Structure of the Book; 2 Exploring Spatio-Temporal Data; 2.1 Spatio-Temporal Data; 2.2 Representation of Spatio-Temporal Data in R; 2.3 Visualization of Spatio-Temporal Data; 2.3.1 Spatial Plots 2.3.2 Time-Series Plots2.3.3 Hovmöller Plots; 2.3.4 Interactive Plots; 2.3.5 Animations; 2.3.6 Trelliscope: Visualizing Large Spatio-Temporal Data Sets; 2.3.7 Visualizing Uncertainty; 2.4 Exploratory Analysis of Spatio-Temporal Data; 2.4.1 Empirical Spatial Means and Covariances; 2.4.2 Spatio-Temporal Covariograms and Semivariograms; 2.4.3 Empirical Orthogonal Functions (EOFs); 2.4.4 Spatio-Temporal Canonical Correlation Analysis; 2.5 Chapter 2 Wrap-Up; Lab 2.1: Data Wrangling; Lab 2.2: Visualization; Lab 2.3: Exploratory Data Analysis; 3 Spatio-Temporal Statistical Models 3.1 Spatio-Temporal Prediction3.2 Regression (Trend-Surface) Estimation; 3.2.1 Model Diagnostics: Dependent Errors; 3.2.2 Parameter Inference for Spatio-Temporal Data; 3.2.3 Variable Selection; 3.3 Spatio-Temporal Forecasting; 3.4 Non-Gaussian Errors; 3.4.1 Generalized Linear Models and Generalized Additive Models; 3.5 Hierarchical Spatio-Temporal Statistical Models; 3.6 Chapter 3 Wrap-Up; Lab 3.1: Deterministic Prediction Methods; Lab 3.2: Trend Prediction; Lab 3.3: Regression Models for Forecasting; Lab 3.4: Generalized Linear Spatio-Temporal Regression 4 Descriptive Spatio-Temporal Statistical Models4.1 Additive Measurement Error and Process Models; 4.2 Prediction for Gaussian Data and Processes; 4.2.1 Spatio-Temporal Covariance Functions; 4.2.2 Spatio-Temporal Semivariograms; 4.2.3 Gaussian Spatio-Temporal Model Estimation; 4.3 Random-Effects Parameterizations; 4.4 Basis-Function Representations; 4.4.1 Random Effects with Spatio-Temporal Basis Functions; 4.4.2 Random Effects with Spatial Basis Functions; 4.4.3 Random Effects with Temporal Basis Functions; 4.4.4 Confounding of Fixed Effects and Random Effects 4.5 Non-Gaussian Data Models with Latent Gaussian Processes4.5.1 Generalized Additive Models (GAMs); 4.5.2 Inference for Spatio-Temporal Hierarchical Models; 4.6 Chapter 4 Wrap-Up; Lab 4.1: Spatio-Temporal Kriging with gstat; Lab 4.2: Spatio-Temporal Basis Functions with FRK; Lab 4.3: Temporal Basis Functions with SpatioTemporal; Lab 4.4: Non-Gaussian Spatio-Temporal GAMs with mgcv; Lab 4.5: Non-Gaussian Spatio-Temporal Models with INLA; 5 Dynamic Spatio-Temporal Models; 5.1 General Dynamic Spatio-Temporal Models; 5.1.1 Data Model; 5.1.2 Process Model; 5.1.3 Parameters
• Publisher Details: Boca Raton, FL : CRC Press
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 519.537; Spatial analysis (Statistics); Statistics; R (Computer program language); MATHEMATICS / Applied; MATHEMATICS / Probability & Statistics / General; Electronic books; Electronic books
• Languages: English
• ISBNs: 9780429649783; 0429649789
• Related ISBNs: 9781138370074
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (EBSCO, Febrary 22, 2019).; Note: Vendor-supplied metadata.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.