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Statistical Learning in Palaeolimnology

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Tracking Environmental Change Using Lake Sediments

Part of the book series: Developments in Paleoenvironmental Research ((DPER,volume 5))

Abstract

This chapter considers a range of numerical techniques that lie outside the familiar statistical methods of linear regression, analysis of variance, and generalised linear models or data-analytical techniques such as ordination, clustering, and partitioning. The techniques outlined have developed as a result of the spectacular increase in computing power since the 1980s. The methods make fewer distributional assumptions than classical statistical methods and can be applied to more complicated estimators and to huge data-sets. They are part of the ever-increasing array of ‘statistical learning’ techniques (sensu Hastie, Tibshirani, Friedman J, The elements of statistical learning, 2nd edn. Springer, New York, 2011) that try to make sense of the data at hand, to detect major patterns and trends, to understand ‘what the data say’, and thus to learn from the data.

A range of tree-based and network-based techniques are presented. These are classification and regression trees, multivariate regression trees, bagged trees, random forests, boosted trees, multivariate adaptive regression splines, artificial neural networks, self-organising maps, Bayesian networks, and genetic algorithms. Principal curves and surfaces are also discussed as they relate to unsupervised self-organising maps. The chapter concludes with a discussion of current developments in shrinkage methods and variable selection in statistical modelling that can help in model selection and can minimise collinearity problems. These include principal components regression, ridge regression, the lasso, and the elastic net.

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Notes

  1. 1.

    Pressure gradient between Los Angeles airport (LAX) and Daggert in mmHg.

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Acknowledgements

We are indebted to Richard Telford, Steve Juggins, and John Smol for helpful comments and/or discussion. Whilst writing this chapter, GLS was supported by the European Union Seventh Framework Programme projects REFRESH (Contract N. 244121) and BioFresh (Contract No. 226874), and by the UK Natural Environment Research Council (grant NE/G020027/1).We are particularly grateful to Cathy Jenks for her editorial help. This is publication A359 from the Bjerknes Centre for Climate Research.

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  1. Environmental Change Research Centre, University College London, Pearson Building, Gower Street, London, WC1E 6BT, UK

    Gavin L. Simpson & H. John B. Birks

  2. Department of Biology and Bjerknes Centre for Climate Research, University of Bergen, 7803, Bergen, N-5020, Norway

    H. John B. Birks

  3. School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK

    H. John B. Birks

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  1. Gavin L. Simpson
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Correspondence to Gavin L. Simpson .

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  1. Allegaten 41, Bergen, 5007, Norway

    H. John B. Birks

  2. Dept. Geobiology, Lab. Palaeobotany and Palynology, University Utrecht, Budapestlaan 4, Utrecht, 3584 CD, Netherlands

    André F. Lotter

  3. , School of Geography,, Newcastle University, Daysh Building 14b, Newcastle-upon-Tyne, NE1 7RU, United Kingdom

    Steve Juggins

  4. Dept. Biology, Paleoecological Environmental, Queen's University, Kingston, K7L 3N6, Ontario, Canada

    John P. Smol

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Simpson, G.L., Birks, H.J.B. (2012). Statistical Learning in Palaeolimnology. In: Birks, H., Lotter, A., Juggins, S., Smol, J. (eds) Tracking Environmental Change Using Lake Sediments. Developments in Paleoenvironmental Research, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2745-8_9

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