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Statistical Analysis of Spatial and Spatio-Temporal Point Patterns: Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Autor Peter J. Diggle
en Limba Engleză Hardback – 23 iul 2013
Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition presents models and statistical methods for analyzing spatially referenced point process data.
Reflected in the title, this third edition now covers spatio-temporal point patterns. It explores the methodological developments from the last decade along with diverse applications that use spatio-temporally indexed data. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences.
This edition also incorporates the use of R through several packages dedicated to the analysis of spatial point process data. Sample R code and data sets are available on the author’s website.
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Specificații

ISBN-13: 9781466560239
ISBN-10: 1466560231
Pagini: 300
Ilustrații: 117 black & white illustrations, 11 black & white tables
Dimensiuni: 156 x 234 x 33 mm
Greutate: 1.12 kg
Ediția:Revizuită
Editura: CRC Press
Colecția CRC Press
Seria Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Locul publicării:Boca Raton, United States

Public țintă

Professional Practice & Development

Cuprins

Introduction. Preliminary Testing. Methods for Sparsely Sampled Patterns. Spatial Point Processes. Nonparametric Methods. Models. Model-Fitting Using Summary Descriptions. Model-Fitting Using Likelihood-Based Methods. Point Process Methods in Spatial Epidemiology. Spatio-Temporal Point Processes. Exploratory Analysis. Empirical Models and Methods. Mechanistic Models and Methods. References.

Notă biografică

Peter Diggle is a Distinguished University Professor and group leader of CHICAS at Lancaster University. Dr. Diggle is also an adjunct professor of biostatistics at both Johns Hopkins University’s and Yale University’s Schools of Public Health, adjunct senior researcher in the International Research Institute for Climate and Society at Columbia University, professor of epidemiology and statistics at the University of Liverpool, a trustee for Biometrika, founding co-editor and advisory board member for Biostatistics, and chair of the Strategic Skills Fellowships Panel of the Medical Research Council. His research focuses on the development and application of statistical methods to the biomedical and health sciences.

Recenzii

"… a valuable addition to the existing literature as it covers a number of topics in point pattern analysis, ranging from the basics of spatial point pattern statistical analysis to more recent developments in the spatio-temporal context. A number of examples are discussed throughout the chapters, which should facilitate the reading for practitioners of applied statistics from various disciplines. Besides this, the fact that data sets and R codes are available online certainly constitutes a nice addition to this application-oriented textbook."
Mathematical Reviews, January 2015
"… well written, concise, and handy. … there are remarkable changes in fundamentals [in this edition]. … All concepts are well illustrated using interesting examples. … an excellent introduction to point process statistics, in particular for beginners."
Biometrical Journal, 2014

Descriere

Retaining all the material from the second edition and adding substantial new material, this third edition presents models and statistical methods for analyzing spatially referenced point process data. Reflected in the title, this edition now covers spatio-temporal point patterns. It also incorporates the use of R through several packages dedicated to the analysis of spatial point process data, with code and data sets available online. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences.