Theory of Spatial Statistics: A Concise Introduction

Theory of Spatial Statistics: A Concise Introduction provides a rigorous mathematical foundation for spatial statistics, with examples from various fields and example code in R. It is suitable for postgraduate and advanced undergraduate students in mathematics and statistics.

\n Format: Paperback / softback
\n Length: 168 pages
\n Publication date: 05 March 2019
\n Publisher: Taylor & Francis Ltd
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Theory of Spatial Statistics: A Concise Introduction delves into the realm of spatial statistics, presenting a comprehensive exploration of the most significant models employed in this field. From a rigorous mathematical standpoint, it elucidates the methodologies for conducting statistical inference, emphasizing the importance of mathematical rigor in understanding spatial data.

Comprehensive in its scope, this text encompasses a wide range of topics, including random fields and point processes. It offers detailed proofs, real-life examples, and theoretical exercises, providing students with a solid foundation for understanding spatial statistics. Additionally, an appendix offers solutions to the exercises, aiding in their comprehension and application.

Designed with an assumption of prior knowledge in probability and statistics, these concise lecture notes serve as a self-contained resource for a semester-long course. They can also serve as a valuable reference book for researchers seeking to delve deeper into the field.

Key Features:

Mathematical Foundations of Spatial Statistics: The book provides a solid foundation in the mathematical principles underlying spatial statistics. It covers essential concepts such as probability distributions, random variables, and spatial autocorrelation, enabling students to grasp the theoretical underpinnings of the field.

Worked Examples: Throughout the text, numerous worked examples are presented, drawn from diverse fields such as mining, disease mapping, forestry, soil and environmental science, and criminology. These examples illustrate the practical applications of spatial statistics and help students connect the theoretical concepts with real-world scenarios.

Literature References: The book includes comprehensive pointers to the literature, facilitating further study and exploration of the subject. It serves as a valuable resource for researchers seeking to delve deeper into specific topics or expand their knowledge base.

Example Code in R: To encourage hands-on experimentation, the book provides example code in the widely-used programming language R. Students can utilize this code to implement the models discussed in the text and gain practical experience in statistical analysis.

Exercises and Solutions: The book includes a comprehensive set of exercises and their solutions, designed to test and deepen students' understanding of spatial statistics. These exercises cover a range of topics, from basic concepts to more advanced applications, and provide valuable opportunities for practice and self-assessment.

Suitable for Postgraduate and Advanced Undergraduate Students: The book is tailored to postgraduate and advanced undergraduate students in mathematics and statistics. It assumes a basic understanding of probability and statistics and provides a comprehensive yet accessible introduction to spatial statistics.

In conclusion, Theory of Spatial Statistics: A Concise Introduction is an essential resource for students and researchers seeking to gain a comprehensive understanding of spatial statistics. With its rigorous mathematical approach, comprehensive coverage, and practical examples, this text provides a solid foundation for advancing knowledge in this field. Whether you are a student embarking on a study of spatial statistics or a researcher seeking to expand your expertise, this book is an invaluable tool for your academic journey.

\n Weight: 290g\n
Dimension: 234 x 157 x 9 (mm)\n
ISBN-13: 9780367146399\n \n