Theory of Spatial Statistics: A Concise Introduction presents the most important models used in spatial statistics, including random fields and point processes, from a rigorous mathematical point of view and shows how to carry out statistical inference. It contains full proofs, real-life examples and theoretical exercises. Solutions to the latter are available in an appendix.
Assuming maturity in probability and statistics, these concise lecture notes are self-contained and cover enough material for a semester course. They may also serve as a reference book for researchers.
Features
* Presents the mathematical foundations of spatial statistics.
* Contains worked examples from mining, disease mapping, forestry, soil and environmental science, and criminology.
* Gives pointers to the literature to facilitate further study.
* Provides example code in R to encourage the student to experiment.
* Offers exercises and their solutions to test and deepen understanding.
The book is suitable for postgraduate and advanced undergraduate students in mathematics and statistics.
