Author: Oliver Schabenberger
Edition: 1
Publisher: Chapman and Hall/CRC
Binding: Hardcover
ISBN: 1584883227
Statistical Methods for Spatial Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. Medical books Statistical Methods for Spatial Data Analysis . It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data.
This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging Medical books Statistical Methods For Spatial Data Analysis Ebook. Statistical Methods for Spatial Data Analysis is a comprehensive treatment of statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. The authors deliver an outstanding treatment of semivariogram estimation and modeling, spatial analysis in the spectral domain, and spatial regression, covering linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data and succinctly discussing Bayesian hierarchical models. The book concludes with a review of simulation, non-stationary covariance, and spatio-temporal processes.
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Statistical Methods for Spatial Data Analysis is a comprehensive treatment of statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. The authors deliver an outstanding treatment of semivariogram estimation and modeling, spatial analysis in the spectral domain, and spatial regression, covering linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data and succinctly discussing Bayesian hierarchical models. The book concludes wit
Statistical Methods For Spatial Data Analysis, ISBN-13: 9781584883227, ISBN-10: 1584883227
Statistical Methods for Spatial Data Analysis
author carol a gotway author oliver schabenberger format hardback language english publication year 20 12 2004 subject mathematics sciences subject 2 mathematics title statistical methods for spatial data analysis author chatfield chris series edited by zidek jim series edited by lindsey jim series edited by schabenberger oliver author gotway carol a author publisher chapman hall publication date dec 22 2004 pages 488 binding hardcover edition 1 st dimensions 6 75 wx 9 75 hx 1 10 d isbn 158488
Medical Book Statistical Methods for Spatial Data Analysis
It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data.
This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields, non-stationary covariance, and spatio-temporal processes.
Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself.
