Handbook of Environmental and Ecological Statistics

• Autore: Gelfand Alan E
• Pubblicazione: Milton : , : CRC Press LLC, , 2019; ©2019
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (882 pages)
• Disciplina: 557.072/7
• Soggetto topico: Environmental sciences - Statistical methods; Ecology - Statistical methods
• Altri autori: FuentesMontserrat HoetingJennifer A SmithRichard Lyttleton
• Nota di contenuto: 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 -- 2.10.1 Bayesian model comparison -- 2.10.2 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 -- 3.4.5 Parameter estimation -- 3.4.6 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 -- 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 -- 5.2.1 Spatial processes, covariance functions, stationarity and isotropy -- 5.2.2 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 -- 6.5.1 Modelling environmental heterogeneity - inhomogeneous Poisson processes and Cox processes -- 6.5.2 Modelling clustering - Neyman Scott processes -- 6.5.3 Modelling inter-individual interaction - Gibbs processes -- 6.5.4 Model fitting - approaches and software -- 6.5.4.1 Approaches -- 6.5.4.2 Relevant software packages -- 6.6 Point processes in ecological applications -- 6.7 Marked point processes - complex data structures -- 6.7.1 Different roles of marks in point patterns -- 6.7.2 Complex models - dependence between marks and patterns -- 6.7.3 Marked point pattern models reflecting the sampling process. ; 6.8 Modelling partially observed point patterns -- 6.8.1 Point patterns observed in small subareas -- 6.8.2 Distance sampling -- 6.9 Discussion -- 6.9.1 Spatial point processes and geo-referenced data -- 6.9.2 Spatial point process modeling and statistical ecology -- 6.9.3 Other data structures -- 6.9.3.1 Telemetry data -- 6.9.3.2 Spatio-temporal patterns -- 6.9.4 Conclusion -- 6.10 Acknowledgments -- 7: Data assimilation -- 7.1 Introduction -- 7.2 Algorithms for data assimilation -- 7.2.1 Optimal interpolation -- 7.2.2 Variational approaches -- 7.2.3 Sequential approaches: the Kalman filter -- 7.3 Statistical approaches to data assimilation -- 7.3.1 Joint modeling approaches -- 7.3.2 Regression-based approaches -- 8: Univariate and Multivariate Extremes for the Environmental Sciences -- 8.1 Extremes and Environmental Studies -- 8.2 Univariate Extremes -- 8.2.1 Theoretical underpinnings -- 8.2.2 Modeling Block Maxima -- 8.2.3 Threshold exceedances -- 8.2.4 Regression models for extremes -- 8.2.5 Application: Fitting a time-varying GEV model to climate model output -- 8.2.5.1 Analysis of individual ensembles and all data -- 8.2.5.2 Borrowing strength across locations -- 8.3 Multivariate Extremes -- 8.3.1 Multivariate EVDs and componentwise block maxima -- 8.3.2 Multivariate threshold exceedances -- 8.3.3 Application: Santa Ana winds and dryness -- 8.3.3.1 Assessing tail dependence -- 8.3.3.2 Risk region occurrence probability estimation -- 8.4 Conclusions -- 9: Environmental Sampling Design -- 9.1 Introduction -- 9.2 Sampling Design for Environmental Monitoring -- 9.2.1 Design framework -- 9.2.2 Model-based design -- 9.2.2.1 Covariance estimation-based criteria -- 9.2.2.2 Prediction-based criteria -- 9.2.2.3 Mean estimation-based criteria -- 9.2.2.4 Multi-objective and entropy-based criteria -- 9.2.3 Probability-based spatial design. ; 9.2.3.1 Simple random sampling -- 9.2.3.2 Systematic random sampling -- 9.2.3.3 Stratified random sampling -- 9.2.3.4 Variable probability sampling -- 9.2.4 Space-filling designs -- 9.2.5 Design for multivariate data and stream networks -- 9.2.6 Space-time designs -- 9.2.7 Discussion -- 9.3 Sampling for Estimation of Abundance -- 9.3.1 Distance sampling -- 9.3.1.1 Standard probability-based designs -- 9.3.1.2 Adaptive distance sampling designs -- 9.3.1.3 Designed distance sampling experiments -- 9.3.2 Capture-recapture -- 9.3.2.1 Standard capture-recapture -- 9.3.2.2 Spatial capture-recapture -- 9.3.3 Discussion -- 10: Accommodating so many zeros: univariate and multivariate data -- 10.1 Introduction -- 10.2 Basic univariate modeling ideas -- 10.2.1 Zeros and ones -- 10.2.2 Zero-inflated count data -- 10.2.2.1 The k-ZIG -- 10.2.2.2 Properties of the k-ZIG model -- 10.2.2.3 Incorporating the covariates -- 10.2.2.4 Model fitting and inference -- 10.2.2.5 Hurdle models -- 10.2.3 Zeros with continuous density G(y) -- 10.3 Multinomial trials -- 10.3.1 Ordinal categorical data -- 10.3.2 Nominal categorical data -- 10.4 Spatial and spatio-temporal versions -- 10.5 Multivariate models with zeros -- 10.5.1 Multivariate Gaussian models -- 10.5.2 Joint species distribution models -- 10.5.3 A general framework for zero-dominated multivariate data -- 10.5.3.1 Model elements -- 10.5.3.2 Specific data types -- 10.6 Joint Attribute Modeling Application -- 10.6.1 Host state and its microbiome composition -- 10.6.2 Forest traits -- 10.7 Summary and Challenges -- 11: Gradient Analysis of Ecological Communities (Ordination) -- 11.1 Introduction -- 11.2 History of ordination methods -- 11.3 Theory and background -- 11.3.1 Properties of community data -- 11.3.2 Coenospace -- 11.3.3 Alpha, beta, gamma diversity -- 11.3.4 Ecological similarity and distance. ; 11.4 Why ordination? -- 11.5 Exploratory analysis and hypothesis testing -- 11.6 Ordination vs. Factor Analysis -- 11.7 A classification of ordination -- 11.8 Informal techniques -- 11.9 Distance-based techniques -- 11.9.1 Polar ordination -- 11.9.1.1 Interpretation of ordination scatter plots -- 11.9.2 Principal coordinates analysis -- 11.9.3 Nonmetric Multidimensional Scaling -- 11.10 Eigenanalysis-based indirect gradient analysis -- 11.10.1 Principal Components Analysis -- 11.10.2 Correspondence Analysis -- 11.10.3 Detrended Correspondence Analysis -- 11.10.4 Contrast between DCA and NMDS -- 11.11 Direct gradient analysis -- 11.11.1 Canonical Correspondence Analysis -- 11.11.2 Environmental variables in CCA -- 11.11.3 Hypothesis testing -- 11.11.4 Redundancy Analysis -- 11.12 Extensions of direct ordination -- 11.13 Conclusions -- II: Topics in Ecological Processes -- 12: Species distribution models -- 12.1 Aims of species distribution modelling -- 12.2 Example data used in this chapter -- 12.3 Single species distribution models -- 12.4 Joint species distribution models -- 12.4.1 Shared responses to environmental covariates -- 12.4.2 Statistical co-occurrence -- 12.5 Prior distributions -- 12.6 Acknowledgments -- 13: Capture-Recapture and distance sampling to estimate population sizes -- 13.1 Basic ideas -- 13.2 Inference for closed populations -- 13.2.1 Censuses and finite population sampling -- 13.2.2 The problem of imperfect detection -- 13.2.3 Capture-recapture on closed populations -- 13.2.4 Distance sampling methods on closed populations -- 13.2.5 N-mixture models for closed populations -- 13.2.6 Count regression -- 13.3 Inference for open populations -- 13.3.1 Crosbie-Manly-Schwarz-Arnason model -- 13.3.2 Cormack-Jolly-Seber model and tag-recovery models -- 13.3.3 Pollock's robust design. ; 13.3.4 Capture recapture models for population growth rate.
• Sommario/riassunto: This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes.
• Titolo autorizzato: Handbook of Environmental and Ecological Statistics
• ISBN: 1-315-15250-9; 1-351-64854-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910969817803321
• Lo trovi qui: Univ. Federico II