Simon Wood

Simon N Wood

School of Mathematics (4.5), University of Bristol, BS8 1TW; +44 (0)117 33 18273 simon.wood _at_ bath.edu

Apologies if I have not replied to you on an mgcv related query: I've got rather behind on mgcv email, especially on the interesting stuff that requires thought.

PhD Opportunity.

I have funding for an EU/UK PhD student starting in September 2017, to work on statistical science/ statistical computing in the area of smooth regression modelling. Projects are available in spatial modelling, mixed modelling and big models for big data. Please get in touch if you are interested (or indeed if you have your own project idea somewhere in my area of interest).

Books

Core Statistics (2015) is a short textbook in the CUP IMS textbook series. The idea is to offer a concise coverage of the essentials that anyone starting a statistics PhD ought to know, in the form of a brief introduction to statistics for the numerate. A pdf version is here (A5 format - ok for e-reading). Try this version for less wasteful printing on A4. Comments (including typo and error reports) very welcome. Here is the errata list and the algae and urchin datasets. (e.g. alg <- read.table("http://www.maths.bris.ac.uk/~sw15190/data/algae.txt") to read directly into R.). If you find the free download useful please consider buying the book (click top right to change location).

Generalized Additive Models: An Introduction with R (2006) provides an introduction to linear models, generalized linear models, generalized additive models and their mixed model extensions. The second edition will appear in spring 2017 (the completed manuscript is with the publishers now). It has a completely revised structure, with greater emphasis on mixed models and the equivalence of smooths and Gaussian random fields. A greatly enhanced range of smoothers is covered, along side a thorough upgrading of the chapter on GAM theory, and many new examples including functional data analysis, survival analysis, location-scale modelling and more.

I work as a professor in the statistics group at the university of Bristol (part time for childcare reasons). I currently hold an EPSRC established career fellowship and have two main research interests.

Smoothing. In particular methods for generalized additive modelling and applications of generalized additive models (GAMs). I am especially interested in smoothness selection, and low rank spline smoothing, and have written an R package called mgcv which implements GAMs. Some recent example smoothing papers are
- Wood, SN (2013) A simple test for random effects in regression models Biometrika 100(4), 1005-1010.
- Wood, SN, N Pya and B Saefken (2016) Smoothing parameter and model selection for general smooth models Journal of the American Statistical Association.
- Wood, SN, Z Li, G Shaddick and NH Augustin (2016) Generalized additive models for gigadata: modelling the UK black smoke network daily data Journal of the American Statistical Association. Here are the data set black_smoke.RData and its description.
Statistical Ecology. In particular using ecological dynamic models as statistical models to help understand ecological mechanisms, and ecological applications of nonlinear random effects models and smooth models, as part of NCSE. Some recent example statistical ecology papers are
- Wood SN (2010) Statistical inference for noisy nonlinear ecological dynamic systems. Nature 466(26): 1102-1104. (or short web version with links to supplementary material and methods ).
- Augustin, NH, V Trenkel, SN Wood and P Lorance (2013) Space-time modelling of blue ling for fisheries stock management Environmetrics 24(2), 109-119.
- M Fasiolo, N Pya and SN Wood (2016) A Comparison of Inferential Methods for Highly Nonlinear State Space Models in Ecology and Epidemiology Statistical Science 31 (1), 96-118

Fuller lists of papers are at researcherid and google scholar . Alternatively if you'd like to see me try to explain what I do whilst tired and anaemic, then here is a university research video. This 2014 BIRS talk on inference for ecological dynamic models is possibly a better bet.

I am interested in taking on PhD students working on any area related to my research interests. Here are a couple of example projects: GAMs for big data and GAMs for multivariate data. The department has funding for strong students.

Advisees:

Matteo Fasiolo, works as a postdoc on smooth modelling for energy forecasting.
Zheyuan Li, works as a PhD student on big data problems in modelling black smoke air pollution data in the UK.
Bertrand Nortier, works on GAM methodology.

Open letter to the USS Trustee board, and the USS AV consultation paper it refers to. (Reproducability: Code to produce the salary RPI plot salary.r, and data used by code: rpi.dat, Pre92salaries.csv.) Also relevant are the technical part of Imperial College's response , LSE's response .

Here is a selection of talks. It's not exhaustive, but hopefully gives some idea of what I work on.

REML estimation of GAMs
GAMs and mgcv
An introduction to GAMs
Implementing mgcv in R
Soap film smoothing how to smooth over domains were the boundary matters.
Inaugural lecture with slides. [12/3/2009. A university of Bath podcast ]
GAM overview [invited talk at the excellent IWSM Barcelona, 2007]
Tensor product smoothing and GAMMs [Given in various forms, in various places].
Calanus in the North Atlantic - a large complex model estimation and testing problem. [St Andrews, RSS and elsewhere].
Slides from a couple of short GAM course .

I am not teaching this year. Here are a couple of examples of previous courses

mgcv - generalized additive (mixed) models and other generalized ridge regression for R.
Software
C.V. (not up to date).
Bristol maps