Statistical methods for trend detection and analysis in the environmental sciences

INDICE: Preface ix Part I METHODOLOGY 1 Introduction 1.1 What is a Trend? 1.2 Why Analyse Trends? 1.3 Some simple examples 1.4 Considerations and Difficulties 1.5 Scope of the book 1.6 Further reading References 2 Exploratory Analysis 2.1 Data Visualisation 2.2 Simple smoothing 2.3 Linear Filters2.4 Classical Test Procedures 2.5 Concluding comments References 3 Parametric Modelling - Deterministic Trends 3.1 The Linear Trend 3.2 Multiple Regression Techniques 3.3 Violations of assumptions 3.4 Nonlinear Trends 3.5 Generalized Linear Models 3.6 Inference with small samples References 4 Nonparametric Trend Estimation4.1 An introduction to nonparametric regression 4.2 Multiple covariates 4.3 Other Nonparametric Estimation Techniques 4.4 Parametric or Nonparametric? References 5 Stochastic Trends 5.1 Stationary Time Series Models and their Properties 5.2 Trend Removal via Differencing 5.3 Long Memory Models 5.4 Models for irregularly spaced series 5.5 State Space and Structural Models 5.6 Nonlinear models References 6 Other Issues 6.1 Multisite Data 6.2 Multivariate series 6.3Point Process Data6.4 Trends in Extremes 6.5 Censored Data References Part IICASE STUDIES 7 Additive Models for Sulphur Dioxide Pollution in Europe 7.1 Introduction 7.2 Additive models with correlated errors 7.3 Models for the SO2 data 7.4 Conclusions References 8 Rainfall trends in southwest Western Australia 8.1 Motivation 8.2 The study region 8.3 Data used in the study 8.4 Modellingmethodology 8.5 Results 8.6 Summary and conclusions References 9 Additive modelling and dynamic factor analysis to estimate commontrends in trophic index time series from the coastal waters in Emilia-Romagna (Adriatic Sea - Italy) 9.1 Introduction 9.2 Data exploration 9.3 Common trends and additive modelling 9.4 Dynamic factor analysis to estimate common trends 9.5 Discussion References10 A Space-Time Study on Forest Health 10.1 Forest health: survey and data 10.2 Regression models for longitudinal data with ordinal responses 10.3 Spatio-temporal models 10.4 Spatio-temporal modelling and analysis of forest health data References Index

• ISBN: 978-0-470-01543-8
• Editorial: John Wiley & Sons
• Encuadernacion: Cartoné
• Páginas: 384
• Fecha Publicación: 18/03/2011
• Nº Volúmenes: 1
• Idioma: Inglés