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Full Description
This book places an emphasis upon statistical methodology, data interpretation, and futureproofing with the intention of advancing statistics for the environment, ecology, and environmental health, in addition to environmental theory and practice, via the application of reliable statistics.
With a focus on advances in statistical methodology and application within the environmental sciences, the overarching purpose of this volume is to illuminate current trends, stimulate a focus on, and connect multidisciplinary domains originating from and within statistical analysis, development, and research. Given that the contributions consist of current improvements and new innovations in climate and environmental science research that are based on statistical theory, researchers can derive inspiration for future advancements or similar analyses on other environmental data.
Authored by internationally renowned scholars, this book is organised in three parts with Part I on Supervised and Unsupervised Learning, Part II on Directional Statistics, and Part III focusing on Spatial and temporal modelling. Primarily intended as a reference book for academic researchers and graduate level students in statistics as well as multidisciplinary domains, the chapters reflect a shared commitment to advancing methodological rigor while addressing real-world environmental concerns. They illustrate how environmental complexity drives the evolution of statistical thinking—and how statistical insight, in turn, informs meaningful action.
Key Features:
· Emphasises the ongoing necessity to progress basic statistical theory and explores its relevance to environmental research.
· Contains multidisciplinary approaches and applications, whetting the appetite for a wider readership than only theoretical statistics.
· Enhances the collective understanding of the ecosystem's diverse perspectives to ensure the welfare of present and future generations.
· Written by renowned subject matter experts and researchers, making it appealing to scholars from diverse fields.
· The statistical framework is not limited to a single methodology based on data complexity but promotes different techniques.
Contents
Editor Biographies List of Contributors Preface Part I: Supervised and Unsupervised Learning Chapter 1: Mapping Emission Dynamics: A High-Dimensional Approach to Cluster Analysis with Outlier Detection Chapter 2: Determining the number of biological species in the presence of spatial patterns of differentiation Chapter 3: Multivariate longitudinal latent Markov models to characterize pollutant exposures Chapter 4: Dirichlet-random forest for predicting compositional data Chapter 5: Robust model selection in mixture regression with application on CO2 emissions data Chapter 6: Bayesian Structure Learning of Directed Acyclic Graphs for Identifying Causal Effects of Weather Elements in South Africa Part II: Directional Statistics Chapter 7: Hidden semi-Markov models for directional time series Chapter 8: Models for Environmental Cylindrical Time Series Chapter 9: A unified approach to optimal model-based detection of change-points with circular data Chapter 10: Analysis of maritime conditions via nonparametric directional methods Chapter 11: Hierarchical Bayesian Models for Multivariate Spatio-Temporal Climate Analysis and Change-Point Detection Part III: Spatial and temporal modelling Chapter 12: Robust Change Point Detection in Air Pollution Chapter 13: Testing of Long-Term Granger Causality in Environmental Time Series Chapter 14: Efficient spatio-temporal Bayesian modeling with INLA Chapter 15: Real-time forecasting of fire front propagation using the level set method and echo state networks Chapter 16: Spatial Meta-Analysis for Finite Populations Chapter 17: A Review of Applications of Extreme Value Theory to Environmental Risk Assessment Chapter 18: A Nonstationary Spatial Count Regression Using Gamma-Count: A Case Study on Canadian Precipitation Chapter 19: More Explorations on a Parametric Model to Assess Segregation in Samples with Small Units Bibliography
