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Many applied researchers equate spatial statistics with prediction or mapping, but this book naturally extends linear models, which includes regression and ANOVA as pillars of applied statistics, to achieve a more comprehensive treatment of the analysis of spatially autocorrelated data.
List of contents
Preface 1. Introduction 2. An Introduction to Covariance Structures for Spatial Linear Models 3. Exploratory Spatial Data Analysis 4. Provisional Estimation of the Mean Structure by Ordinary Least Squares 5. Generalized Least Squares Estimation of the Mean Structure 6. Parametric Covariance Structures for Geostatistical Models 7. Parametric Covariance Structures for Spatial-Weights Linear Models 8. Likelihood-Based Inference 9. Spatial Prediction 10. Spatial Sampling Design 11. Analysis and Design of Spatial Experiments 12. Extensions Appendix A: Some Matrix Results
About the author
Dale L. Zimmerman is Professor of Statistics at the University of Iowa, and Jay M. Ver Hoef is Senior Scientist and Statistician, Alaska Fisheries Science Center, NOAA Fisheries. Both are Fellows of the American Statistical Association and winners of that association’s Section for Statistics and the Environment Distinguished Achievement Award.
Summary
Many applied researchers equate spatial statistics with prediction or mapping, but this book naturally extends linear models, which includes regression and ANOVA as pillars of applied statistics, to achieve a more comprehensive treatment of the analysis of spatially autocorrelated data.
