Spatial Linear Models for Environmental Data
Language: English
Published by Chapman and Hall/CRC, 2024
- Hardcover
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Spatial Linear Models for Environmental Data | Dale L. Zimmerman (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2024 | Chapman and Hall/CRC | EAN 9780367183349 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Seller Inventory # 128712135
- Title
- Spatial Linear Models for Environmental Data
- Author
- Dale L. Zimmerman (u. a.)
- Publisher
- Chapman and Hall/CRC
- Publication year
- 2024
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 036718334X
- ISBN 13
- 9780367183349
- Item weight
- 945 grams
- Dimensions
- 260 x 183 x 26 mm
- Seller catalogs
- Bücher
Many applied researchers equate spatial statistics with prediction or mapping, but this book naturally extends linear models, which includes regression and ANOVA as pillars of applied statistics, to achieve a more comprehensive treatment of the analysis of spatially autocorrelated data. Spatial Linear Models for Environmental Data, aimed at students and professionals with a master’s level training in statistics, presents a unique, applied, and thorough treatment of spatial linear models within a statistics framework. Two subfields, one called geostatistics and the other called areal or lattice models, are extensively covered. Zimmerman and Ver Hoef present topics clearly, using many examples and simulation studies to illustrate ideas. By mimicking their examples and R code, readers will be able to fit spatial linear models to their data and draw proper scientific conclusions.
Topics covered include:
- Exploratory methods for spatial data including outlier detection, (semi)variograms, Moran’s I, and Geary’s c.
- Ordinary and generalized least squares regression methods and their application to spatial data.
- Suitable parametric models for the mean and covariance structure of geostatistical and areal data.
- Model-fitting, including inference methods for explanatory variables and likelihood-based methods for covariance parameters.
- Practical use of spatial linear models including prediction (kriging), spatial sampling, and spatial design of experiments for solving real world problems.
All concepts are introduced in a natural order and illustrated throughout the book using four datasets. All analyses, tables, and figures are completely reproducible using open-source R code provided at a GitHub site. Exercises are given at the end of each chapter, with full solutions provided on an instructor’s FTP site supplied by the publisher.
"Synopsis" may belong to another edition of this title.
About the Author
Dale L. Zimmerman is Professor of Statistics at the University of Iowa, and Jay M. Ver Hoef is Senior Scientist and Statistician, Alaska Fisheries Science Center, NOAA Fisheries. Both are Fellows of the American Statistical Association and winners of that association’s Section for Statistics and the Environment Distinguished Achievement Award.
"About the title" may belong to another edition of this title.
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