Environmental Modelling with Contemporary Statistics
Chapman & Hall/CRC (Verlag)
978-1-032-90391-0 (ISBN)
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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.
Professor Andriëtte Bekker is an emeritus professor and former Head of the Department of Statistics at the University of Pretoria (2012–2022). A recipient of the S2A3 Medal for scientific achievement, she is internationally recognised for her contributions to multivariate and matrix variate distribution theory, with expertise spanning directional statistics, model-based clustering, and graphical network modelling. She has authored over 130 peer-reviewed publications and edited volumes advancing statistical methodology and computation. Professor Bekker is an elected member of the International Statistical Institute and leads the Statistical Theory and Applied Statistics focus area within the DSTI-NRF Centre of Excellence in Mathematical and Statistical Sciences. Her recent accolades include the University of Pretoria’s Exceptional Academic Achiever Award (2023), a fellowship from the South African Statistical Association (2024), and an NRF rating as a researcher of international standing. Dr. Priyanka Nagar is a Senior Lecturer in Statistics and Actuarial Science at Stellenbosch University. She holds a PhD in Mathematical Statistics from the University of Pretoria. Her research focuses on statistical learning theory, directional statistics, and copula-based modelling, with applications in environmental, biomechanical, and energy domains. Dr Nagar’s work advances statistical methodologies for complex environmental systems. Drawing on experience in both academia and industry, she brings a rigorous and applied perspective to statistical analysis. She is actively engaged in mentoring, supervision, and strengthening statistical capacity within environmental statistics research across South Africa. Johan Ferreira is a Professor in the School of Statistics and Actuarial Science at the University of the Witwatersrand, and previously served as the Assistant Focus Area Coordinator for the Statistical Theory and Applied Statistics focus area of the Centre of Excellence in Mathematical and Statistical Science, based at the University of the Witwatersrand in Johannesburg. He is an ASLP 4.1/4.2 fellow of Future Africa and was identified as one of the Top 200 South Africans under the age of 35 by the Mail & Guardian newspaper in the Education category. Johan regularly published in peer-reviewed, accredited journals, and his research interests include the probabilistic modelling of entropy, meaningful mixture modelling, directional statistics, and topics in educational statistics. Professor Barend Erasmus is an ecologist with broad experience in climate change adaptation. His publication record reflects his interests in interdisciplinary work. His doctoral degree at the University of Pretoria on assessing impacts of climate change impacts on biodiversity in South Africa, remains relevant in international literature. Over time, his research interests expanded from climate change impacts, to broader sustainability issues across a wide range of sectors. His current academic work is on exploring the risks and opportunities of rapidly developing climate science for business and industry. He is passionate about postgraduate training, and students are deeply embedded in collaborative and interdisciplinary research programmes. Professor Abel Ramoelo is an Executive Director of the Earth Observation Programme at the South African National Space Agency (SANSA) and an Extraordinary Professor at the University of Pretoria. He has a PhD in Geoinformation Science and Earth Observation from the University of Twente, the Netherlands. He leads a dynamic team focused on developing earth intelligence to address the societal challenges we face today. He previously worked at the CSIR, advancing from junior to principal researcher, at SANParks as a regional ecologist/ remote sensing specialist, and at the University of Pretoria as an associate professor and Director of the Centre for Environmental Studies in the Department of Geography, Geoinformatics, and Meteorology.
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
| Erscheint lt. Verlag | 11.6.2026 |
|---|---|
| Zusatzinfo | 48 Tables, black and white; 60 Line drawings, color; 18 Line drawings, black and white; 13 Halftones, color; 1 Halftones, black and white; 73 Illustrations, color; 19 Illustrations, black and white |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| Naturwissenschaften ► Biologie ► Ökologie / Naturschutz | |
| Naturwissenschaften ► Geowissenschaften ► Geologie | |
| ISBN-10 | 1-032-90391-0 / 1032903910 |
| ISBN-13 | 978-1-032-90391-0 / 9781032903910 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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