Handbook of environmental and ecological statistics

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