Spatial analysis / John T. Kent, Kanti V. Mardia.

By: Kent, J. T. (John T.) [author.]
Contributor(s): Mardia, K. V [author.]
Language: English Series: Wiley series in probability and statistics: Publisher: Hoboken, NJ : John Wiley & Sons, Inc., 2022Copyright date: ©2022Description: 1 online resource (xxviii, 372 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9780471632054 ; 9781118763551; 1118763556; 9781118763575; 1118763572; 9781118763568; 1118763564Subject(s): Spatial analysis (Statistics)Genre/Form: Electronic books.DDC classification: 001.4/22 LOC classification: QA278.2 | .K46 2022Online resources: Full text available at Wiley Online Library Click here to view.
Contents:
Appendix A: Mathematical background 357 A.1 Domains for sequences and functions 357 A.2 Classes of sequences and functions 358 A.2.1 Functions on the domain Rd 359 A.2.2 Sequences on the domain Zd 359 A.2.3 Classes of functions on the domain Sd 1 360 A.2.4 Classes of sequences on the domain Zd N, where N = (n[1]; : : : ; n[d]) 360 A.3 Matrix algebra 360 A.3.1 The spectral decomposition theorem 360 A.3.2 Moore-Penrose generalized inverse 361 A.3.3 Orthogonal projection matrices 362 A.3.4 Partitioned matrices 363 A.3.5 Schur product 364 A.3.6 Woodbury formula for a matrix inverse 364 A.3.7 Quadratic forms 365 A.3.8 Toeplitz and circulant matrices 366 A.3.9 Tensor product matrices 367 A.3.
Summary: "This book provides insight into the statistical investigation of the interdependence of random variables as a function of their proximity in space and time. Interest in this area is being fuelled by the growing need to analyse data from many fields of application."-- Provided by publisher.
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Includes bibliographical references and index.

Appendix A: Mathematical background 357 A.1 Domains for sequences and functions 357 A.2 Classes of sequences and functions 358 A.2.1 Functions on the domain Rd 359 A.2.2 Sequences on the domain Zd 359 A.2.3 Classes of functions on the domain Sd 1 360 A.2.4 Classes of sequences on the domain Zd N, where N = (n[1]; : : : ; n[d]) 360 A.3 Matrix algebra 360 A.3.1 The spectral decomposition theorem 360 A.3.2 Moore-Penrose generalized inverse 361 A.3.3 Orthogonal projection matrices 362 A.3.4 Partitioned matrices 363 A.3.5 Schur product 364 A.3.6 Woodbury formula for a matrix inverse 364 A.3.7 Quadratic forms 365 A.3.8 Toeplitz and circulant matrices 366 A.3.9 Tensor product matrices 367 A.3.

"This book provides insight into the statistical investigation of the interdependence of random variables as a function of their proximity in space and time. Interest in this area is being fuelled by the growing need to analyse data from many fields of application."-- Provided by publisher.

About the Author
John T. Kent is a Professor in the Department of Statistics at the University of Leeds, UK. He began his career as a research fellow at Sidney Sussex College, Cambridge before moving to the University of Leeds. He has published extensively on various aspects of statistics, including infinite divisibility, directional data analysis, multivariate analysis, inference, robustness, shape analysis, image analysis, spatial statistics, and spatial-temporal modelling.

Kanti V. Mardia is a Senior Research Professor and Leverhulme Emeritus Fellow in the Department of Statistics at the University of Leeds, and a Visiting Professor at the University of Oxford. During his career he has received many prestigious honours, including in 2003 the Guy Medal in Silver from the Royal Statistical Society, and in 2013 the Wilks memorial medal from the American Statistical Society. His research interests include bioinformatics, directional statistics, geosciences, image analysis, multivariate analysis, shape analysis, spatial statistics, and spatial-temporal modelling.
Kent and Mardia are also joint authors of a well-established monograph on Multivariate Analysis.

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