Simon N Wood
School of Mathematics (4.5),
University of Bristol, BS8 1TW; +44 (0)117 33 18273
simon.wood _at_ bath.edu
Many things are possible with infinite data and infinite computer time in infinite precision. I am mostly interested in the case of finite data, finite computer time and finite precision.
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
USS pension dispute
I have tried on several occasions to check the claims that the USS pension fund is in massive deficit. So far I have not been able to reproduce, such a deficit. Here is a note on a simple pension fund model which includes the main features of USS. The model code (R) is model2.r, while the demographic data is in female-lt1416.txt, male-lt1416.txt and gap.dat. If there is a genuine problem, it ought to be possible to include the feature producing it in this model, and demonstrate it.
The following relates to the previous USS dispute, but the basic issues are similar. 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 .
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 Bristol (part time for childcare reasons). 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 tend to think of the British Empire as a racist enterprise responsible for the death of millions and the subjugation of far more, rather than, say, an indication of the country's former greatness. I'm currently joint editor of JRSSB and have two main research interests.
Fuller lists of papers are at
google scholar .
Here is a 2014 BIRS talk on inference for ecological dynamic models.
- 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
which implements GAMs. Some recent example smoothing papers are
- Statistical Ecology. In particular using ecological dynamic
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
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.
- 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.
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 Theory of Inference. Here are a couple of examples of previous courses.