Handbook of environmental and ecological statistics / Alan E. Gelfand, Montserrat Fuentes, Jennifer A. Hoesting, Richard Smith.
"This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in the environm...
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Full Text (via EBSCO) |
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| Main Authors: | , , , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton, FL :
CRC Press,
2019.
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| Edition: | First edition. |
| Series: | Chapman & Hall/CRC handbooks of modern statistical methods.
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| Subjects: |
MARC
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| 245 | 1 | 0 | |a Handbook of environmental and ecological statistics / |c Alan E. Gelfand, Montserrat Fuentes, Jennifer A. Hoesting, Richard Smith. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, |c 2019. | |
| 300 | |a 1 online resource : |b text file, PDF. | ||
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| 545 | 0 | |a Alan E. Gelfand is the James B. Duke Professor of Statistical Science at Duke University. He is a leader in Bayesian spatial modeling and analysis including a successful book in this area with Banerjee and Carlin. Montserrat (Montse) Fuentes, Ph. D., became dean of the Virginia Commonwealth University College of Humanities and Sciences on July 1, 2016. She came to VCU from North Carolina State University, where she served as the head of the Department of Statistics and James M. Goodnight Distinguished Professor of Statistics. She also served as center director for the Research Network for Statistical Methods for Atmospheric and Oceanic Sciences, a research collaborative funded by the National Science Foundation. She received a dual bachelor's degree in mathematics and music (piano) from the University of Valladolid in Spain and a Ph. D. in statistics from the University of Chicago. Jennifer A. Hoeting is a Professor of Statistics at Colorado State University, where she has worked since 1994. She received her PhD from the University of Washington. Richard L. Smith is Mark L. Reed III Distinguished Professor of Statistics and Professor of Biostatistics in the University of North Carolina, Chapel Hill. From 2010-2017 he was also Director of the Statistical and Applied Mathematical Sciences Institute, a Mathematical Sciences Institute supported by the National Science Foundation, and he will continue (through June 2018) as Associate Director of SAMSI. He obtained his PhD from Cornell University and previously held academic positions at Imperial College (London), the University of Surrey (Guildford, England) and Cambridge University. His main research interest is environmental statistics and associated areas of methodological research such as spatial statistics, time series analysis and extreme value theory. He is particularly interested in statistical aspects of climate change research, and in air pollution including its health effects. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, an Elected Member of the International Statistical Institute, and has won the Guy Medal in Silver of the Royal Statistical Society, and the Distinguished Achievement Medal of the Section on Statistics and the Environment, American Statistical Association. In 2004 he was the J. Stuart Hunter Lecturer of The International Environmetrics Society (TIES). He is also a Chartered Statistician of the Royal Statistical Society. | |
| 505 | 0 | |a Cover; Half Title; Title Page; Copyright Page; Table of Contents; Preface; 1: Introduction; I: Methodology for Statistical Analysis of Environmental Processes; 2: Modeling for environmental and ecological processes; 2.1 Introduction; 2.2 Stochastic modeling; 2.3 Basics of Bayesian inference; 2.3.1 Priors; 2.3.2 Posterior inference; 2.3.3 Bayesian computation; 2.4 Hierarchical modeling; 2.4.1 Introducing uncertainty; 2.4.2 Random effects and missing data; 2.5 Latent variables; 2.6 Mixture models; 2.7 Random effects; 2.8 Dynamic models; 2.9 Model adequacy; 2.10 Model comparison. | |
| 505 | 8 | |a 2.10.1 Bayesian model comparison2.10.2 Model comparison in predictive space; 2.11 Summary; 3: Time series methodology; 3.1 Introduction; 3.2 Time series processes; 3.3 Stationary processes; 3.3.1 Filtering preserves stationarity; 3.3.2 Classes of stationary processes; 3.3.2.1 IID noise and white noise; 3.3.2.2 Linear processes; 3.3.2.3 Autoregressive moving average processes; 3.4 Statistical inference for stationary series; 3.4.1 Estimating the process mean; 3.4.2 Estimating the ACVF and ACF; 3.4.3 Prediction and forecasting; 3.4.4 Using measures of correlation for ARMA model identification. | |
| 505 | 8 | |a 3.4.5 Parameter estimation3.4.6 Model assessment and comparison; 3.4.7 Statistical inference for the Canadian lynx series; 3.5 Nonstationary time series; 3.5.1 A classical decomposition for nonstationary processes; 3.5.2 Stochastic representations of nonstationarity; 3.6 Long memory processes; 3.7 Changepoint methods; 3.8 Discussion and conclusions; 4: Dynamic models; 4.1 Introduction; 4.2 Univariate Normal Dynamic Linear Models (NDLM); 4.2.1 Forward learning: the Kalman filter; 4.2.2 Backward learning: the Kalman smoother; 4.2.3 Integrated likelihood; 4.2.4 Some properties of NDLMs. | |
| 505 | 8 | |a 4.2.5 Dynamic generalized linear models (DGLM)4.3 Multivariate Dynamic Linear Models; 4.3.1 Multivariate NDLMs; 4.3.2 Multivariate common-component NDLMs; 4.3.3 Matrix-variate NDLMs; 4.3.4 Hierarchical dynamic linear models (HDLM); 4.3.5 Spatio-temporal models; 4.4 Further aspects of spatio-temporal modeling; 4.4.1 Process convolution based approaches; 4.4.2 Models based on stochastic partial differential equations; 4.4.3 Models based on integro-difference equations; 5: Geostatistical Modeling for Environmental Processes; 5.1 Introduction; 5.2 Elements of point-referenced modeling. | |
| 505 | 8 | |a 5.2.1 Spatial processes, covariance functions, stationarity and isotropy5.2.2 Anisotropy and nonstationarity; 5.2.3 Variograms; 5.3 Spatial interpolation and kriging; 5.4 Summary; 6: Spatial and spatio-temporal point processes in ecological applications; 6.1 Introduction -- relevance of spatial point processes to ecology; 6.2 Point processes as mathematical objects; 6.3 Basic definitions; 6.4 Exploratory analysis -- summary characteristics; 6.4.1 The Poisson process-a null model; 6.4.2 Descriptive methods; 6.4.3 Usage in ecology; 6.5 Point process models. | |
| 505 | 8 | |a 6.5.1 Modelling environmental heterogeneity -- inhomogeneous Poisson processes and Cox processes. | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | 2 | |a "This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in the environmental processes. In addition, the environmental community has substantially increased its scope of data collection including, e.g., observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial. The contribution of this handbook is to assemble, in roughly 35 chapters, a state-ofthe-art view of this interface"--Provided by publisher. | |
| 650 | 0 | |a Environmental sciences |x Statistical methods. |0 http://id.loc.gov/authorities/subjects/sh2009103465. | |
| 650 | 0 | |a Ecology |x Statistical methods. |0 http://id.loc.gov/authorities/subjects/sh2008118584. | |
| 650 | 7 | |a Ecology |x Statistical methods. |2 fast |0 (OCoLC)fst00901539. | |
| 650 | 7 | |a Environmental sciences |x Statistical methods. |2 fast |0 (OCoLC)fst00913513. | |
| 700 | 1 | |a Fuentes, Montse, |e author. |0 http://id.loc.gov/authorities/names/no2019042063. | |
| 700 | 1 | |a Hoeting, Jennifer A. |q (Jennifer Ann), |d 1966- |e author. |0 http://id.loc.gov/authorities/names/no2005017715 |1 http://isni.org/isni/0000000118834722. | |
| 700 | 1 | |a Smith, Richard Lyttleton, |e author. | |
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