activity
20162020
most citedCOVID-19 and the difficulty of inferring epidemiological parameters from clinical data

14 citations · 20 across the 2 of their papers we have counts for

collaborators

7 papers

stat.ME20206 cited

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.…

stat.AP2020

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…

q-bio.QM202014 cited

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…

stat.ME2018

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…

stat.CO2016

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…

stat.ML2016

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…