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Statistical methods for trend detection and analysis in the environmental sciences / Richard E. Chandler, E. Marian Scott. — Chichester : Wiley, 2011. – (58.181/C456) |
Contents
Contents
Preface
Contributing authors
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 filters
2.4 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 Generalised linear models
3.6 Inference with small samples
References
4 Nonparametric trend estimation
4.1 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
6.3 Point process data
6.4 Trends in extremes
6.5 Censored data
References
Part II CASE 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
Acknowledgement
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 Modelling methodology
8.5 Results
8.6 Summary and conclusions
References
9 Estimation of common trends for trophic index series
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
Acknowledgement
References
10 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 Spatiotemporal models
10.4 Spatiotemporal modelling and analysis of forest health data
Acknowledgements
References
Index
