1.4k citations · 1.9k across the 16 of their papers we have counts for
18 papers
Modelling tree survival for investigating climate change effects
Nicole N. Augustin, Axel Albrecht, Karim Anaya-Izquierdo +4
Using German forest health monitoring data we investigate the main drivers leading to tree mortality and the association between defoliation and mortality; in particular (a) whethe…
A note on the modeling of the effects of experimental time in psycholinguistic experiments
R. Harald Baayen, Matteo Fasiolo, Simon Wood +1
Thul et al. (2020) called attention to problems that arise when chronometric experiments implementing specific factorial designs are analysed with the generalized additive mixed mo…
Spatial+: a novel approach to spatial confounding
Emiko Dupont, Simon N. Wood, Nicole Augustin
In spatial regression models, collinearity between covariates and spatial effects can lead to significant bias in effect estimates. This problem, known as spatial confounding, is e…
qgam: Bayesian non-parametric quantile regression modelling in R
Matteo Fasiolo, Simon N. Wood, Margaux Zaffran +2
Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package.…
Additive stacking for disaggregate electricity demand forecasting
Christian Capezza, Biagio Palumbo, Yannig Goude +2
Future grid management systems will coordinate distributed production and storage resources to manage, in a cost effective fashion, the increased load and variability brought by th…
COVID-19 and the difficulty of inferring epidemiological parameters from clinical data
Simon N. Wood, Ernst C. Wit, Matteo Fasiolo +1
Knowing the infection fatality ratio (IFR) is of crucial importance for evidence-based epidemic management: for immediate planning; for balancing the life years saved against the l…
Inferring UK COVID-19 fatal infection trajectories from daily mortality data: were infections already in decline before the UK lockdowns?
Simon N. Wood
The number of new infections per day is a key quantity for effective epidemic management. It can be estimated relatively directly by testing of random population samples. Without s…
Scalable visualisation methods for modern Generalized Additive Models
Matteo Fasiolo, Raphaël Nedellec, Yannig Goude +1
In the last two decades the growth of computational resources has made it possible to handle Generalized Additive Models (GAMs) that formerly were too costly for serious applicatio…
Fast calibrated additive quantile regression
M. Fasiolo, S. N. Wood, M. Zaffran +2
We propose a novel framework for fitting additive quantile regression models, which provides well calibrated inference about the conditional quantiles and fast automatic estimation…
P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data
Simon N. Wood
The P-splines of Eilers and Marx (1996) combine a B-spline basis with a discrete quadratic penalty on the basis coefficients, to produce a reduced rank spline like smoother. P-spli…
Computing AIC for black-box models using Generalised Degrees of Freedom: a comparison with cross-validation
Severin Hauenstein, Carsten F. Dormann, Simon N Wood
Generalised Degrees of Freedom (GDF), as defined by Ye (1998 JASA 93:120-131), represent the sensitivity of model fits to perturbations of the data. As such they can be computed fo…
A note on basis dimension selection in generalized additive modelling
Natalya Pya, Simon N Wood
Two new approaches for checking the dimension of the basis functions when using penalized regression smoothers are presented. The first approach is a test for adequacy of the basis…
Just Another Gibbs Additive Modeller: Interfacing JAGS and mgcv
Simon N Wood
The BUGS language offers a very flexible way of specifying complex statistical models for the purposes of Gibbs sampling, while its JAGS variant offers very convenient R integratio…
Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models
R. Harald Baayen, Jacolien van Rij, Cecile de Cat +1
A problem that tends to be ignored in the statistical analysis of experimental data in the language sciences is that responses often constitute time series, which raises the proble…
An Extended Empirical Saddlepoint Approximation for Intractable Likelihoods
Matteo Fasiolo, Simon N. Wood, Florian Hartig +1
The challenges posed by complex stochastic models used in computational ecology, biology and genetics have stimulated the development of approximate approaches to statistical infer…
Approximate methods for dynamic ecological models
Matteo Fasiolo, Simon N. Wood
This document is due to appear as a chapter of the forthcoming Handbook of Approximate Bayesian Computation (ABC) by S. Sisson, L. Fan, and M. Beaumont. Here we describe some of th…
Smoothing parameter and model selection for general smooth models
Simon N. Wood, Natalya Pya, Benjamin Säfken
This paper discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaus…
A comparison of inferential methods for highly non-linear state space models in ecology and epidemiology
Matteo Fasiolo, Natalya Pya, Simon N. Wood
Highly non-linear, chaotic or near chaotic, dynamic models are important in fields such as ecology and epidemiology: for example, pest species and diseases often display highly non…