Simon N Wood
Chair of Computational Statistics, School of Mathematics, James Clerk Maxwell Building,
University of Edinburgh, EH9 3FD
simon.wood ed.ac.uk
I'm not on X Twitter: too much fast thinking, availability and confirmation bias. I also think social justice is about economic inequality - it's a mystery why the Silicon Valley tech billionaires don't agree.
I work as a professor of statistics. Statistics is about the honest interpretation of data, which is much less appealing than less honest interpretation. "There are lies, damned lies and statistics" as Disraeli possibly said. Gladstone was definitely president of the Royal Statistical Society. Probably both right.
Apologies for the slow mgcv email reply rate: I've got rather behind, especially on the interesting stuff that
requires thought.
What just happened? Risk, Covid, lockdowns and semi-parametric methods plenary RSS 22 talk. A comment on Dominic Cummings' Covid enquiry evidence and old covid material.
How a heatpump works using only basic school physics.
4 lectures on penalized regression
These contain animations which unfortunately can only be viewed using an Adobe reader (the ancient version 9 still available for Linux does work).
- penalized regression with ridge regression and the Lasso.
- smoothing with spline type smoothers.
- generalized additive models.
- INLA, backfitting and boosting approaches.
Notes on debugging R code and C code from R
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 (2nd ed) (2017) provides an introduction to linear (mixed) models, generalized linear (mixed) models, generalized additive models and their mixed model extensions. The second edition 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 Edinburgh. I think Brexit was probably a rather poor decision. I kind of liked being in a block that produces roughly 100% of its own food, as opposed to an island that produces roughly 60%, and it seemed like a sensible thing to be part of a group large enough to stand up to China and the USA. But then I also have mixed feelings about British Imperial history (not as bad as the Belgian Congo, and eventually did the right thing on suppressing the slave trade, but the Opium Wars, Boer concentration camps, Irish and Bengal Famines, rapacious resource extraction, and earlier slave trade involvement are hardly matters for pride). I was co-editor of JRSSB 2018-2021 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 example smoothing papers are
- Wood, SN, N Pya and B Saefken (2016) Smoothing parameter and model selection for general smooth models (with discussion). Journal of the American Statistical Association.
- Wood, SN, Z Li, G Shaddick and NH Augustin (2017) 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.
- Wood, SN and M Fasiolo (2017) A Generalized Fellner-Schall Method for Smoothing Parameter Optimization with Application to Tweedie Location, Scale and
Shape Models Biometrics.
- Wood, SN (2020) Inference and computation with generalized additive
models and their extensions (with discussion from Greven, Scheipl, Kneib and Eilers) TEST 29(2) 307-339.
- Wood, SN (submitted 13 June 2023) On Neighbourhood Cross Validation.
- Statistical Ecology/Epidemiology. In particular using biological dynamic
models as
statistical models to help understand biological mechanisms. Examples:
Fuller lists of papers are at scholargps,
researcherid or
google scholar .
Here is a 2014 BIRS talk on inference for ecological dynamic models.
Boring career stuff about me: state comprehensive school educated; BSc in Physics from Manchester; PhD on biological modelling (Dept Applied Physics Strathclyde); short stint (1989-90) as a civil service bioeconomic modeller MAFF; 4 years postdoc at Imperial (biology) on biological dynamic models. Lecturer - Reader in statistical ecology, St Andrews (maths), also RSS graduate diploma in statistics; Reader - Prof in statistics (Glasgow, Bath, Bristol).
Here is a selection of talks. It's not exhaustive, but hopefully gives
some idea of what I work on.
This year I'm teaching Statistical Programming. Here are a couple of examples of previous courses.