Intro; Preface; Contents; Fast Bayesian Classification for Disease Mapping and the Detection of Disease Clusters; 1 Introduction; 2 Scan Methods for the Detection of Disease Clusters; 2.1 Spatial Scan Methods; 2.2 Classification of Disease; 3 Classification of Disease: Generalised Linear Models; 3.1 Adjustment for Relevant Covariates; 4 Bayesian Hierarchical Models for the Detection of Disease Clusters; 4.1 Detection of Clusters; 4.2 Cluster Selection; 4.3 Number of Clusters; 5 Classification of Disease: Generalised Mixed-Effects Models; 5.1 Motivation; 5.2 GLM with Random Effects

5.3 Selection and Number of Clusters Using GLMM6 Classification of Disease: Zero-Inflated Models; 6.1 Cluster Selection; 7 Space-Time Clusters; 8 Simulation Study; 9 Examples; 9.1 Cancer in Upstate New York; 9.1.1 Spatial Scan Statistic; 9.1.2 Cluster Selection Using GLMs; 9.1.3 Cluster Selection Using GLMMs; 9.2 Analysis of Zero-Inflated Data: Brain Cancer in Navarre, Spain; 10 Discussion; References; A Novel Hierarchical Multinomial Approach to Modeling Age-Specific Harvest Data; 1 Introduction; 2 Age-Specific Harvest Data; 3 Model Development; 3.1 Harvest Leslie Matrix Model

3.2 Beta Distribution-Based Hierarchical Multinomial Model4 Simulation Study; 4.1 Generating Data; 4.1.1 Matrix Parameter Settings; 4.1.2 Matrix Randomness Settings; 4.2 Results; 5 Motivating Example; 6 Conclusion; Appendix; References; Detection of Change Points in Spatiotemporal Data in the Presence of Outliers and Heavy-Tailed Observations; 1 Introduction; 2 The GSTAR Model-Based Procedure of Change-Point Detection in the Daily Spatiotemporal Data; 2.1 The GSTAR Model; 2.2 The Estimation; 2.2.1 Initial Values; 2.2.2 The MEM-Type Algorithm; 2.3 The Change-Point Detection Procedure

3 Application3.1 A Real Data Example; 3.2 A Simulated Example; 3.2.1 Data with Outliers; 3.2.2 The Change-Point Detection; 4 Conclusions; Appendix; References; Modeling Spatiotemporal Mismatch for Aerosol Profiles; 1 Introduction; 2 Metrology of a Data Comparison and Associated Errors; 3 Aerosol Profiles: Comparison of CALIOP/CALIPSO and EARLINET; 3.1 CALIOP/CALIPSO Description; 3.1.1 CALIOP Sampling; 3.1.2 CALIOP Smoothing; 3.2 EARLINET Description; 3.2.1 EARLINET Sampling; 3.2.2 EARLINET Smoothing; 4 Comparison Setup; 5 Horizontal Smoothing; 6 Vertical Splitting; 7 Conclusions; References

A Spatiotemporal Approach for Predicting Wind Speed Along the Coast of Valparaiso, Chile1 Introduction; 2 Preliminary Analysis; 2.1 The Data Sets; 2.2 Preliminary Statistical Analysis; 2.3 The Data Sets; 3 Space-Time Regression Modeling; 4 Results; 4.1 Spatiotemporal Estimation; 5 Conclusions and Further Developments; References; Spatiotemporal Precipitation Variability Modeling in the Blue Nile Basin: 1998-2016; 1 Introduction; 2 Data; 2.1 Tropical Precipitation Measuring Mission (TRMM); 2.2 Large-Scale Atmospheric and Climate Indices; 3 Methods; 3.1 Space-Time Empirical Orthogonal Function Analysis