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@misc{covid_europe, author = {Flaxman, S and Mishra, S and Gandy, A and Unwin, H and Coupland, H and Mellan, T and Zhu, H and Berah, T and Eaton, J and Perez Guzman, P and Schmit, N and Cilloni, L and Ainslie, K and Baguelin, M and Blake, I and Boonyasiri, A and Boyd, O and Cattarino, L and Ciavarella, C and Cooper, L and Cucunuba Perez, Z and Cuomo-Dannenburg, G and Dighe, A and Djaafara, A and Dorigatti, I and Van Elsland, S and Fitzjohn, R and Fu, H and Gaythorpe, K and Geidelberg, L and Grassly, N and Green, W and Hallett, T and Hamlet, A and Hinsley, W and Jeffrey, B and Jorgensen, D and Knock, E and Laydon, D and Nedjati Gilani, G and Nouvellet, P and Parag, K and Siveroni, I and Thompson, H and Verity, R and Volz, E and Walters, C and Wang, H and Wang, Y and Watson, O and Winskill, P and Xi, X and Whittaker, C and Walker, P and Ghani, A and Donnelly, C and Riley, S and Okell, L and Vollmer, M and Ferguson, N and Bhatt, S}, title = {Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries}, doi = {https://doi.org/10.25561/77731} }

@misc{MCunit, title = {Unit Testing for {MCMC} and other {M}onte {C}arlo Methods}, author = {Axel Gandy and James Scott}, year = {2020}, eprint = {2001.06465}, archiveprefix = {arXiv}, primaryclass = {stat.ME}, rpackage = {https://bitbucket.org/agandy/mcunit/} }

@misc{weather_m_out_of_n, title = {Quantifying demand and weather uncertainty in power system models using the m out of n bootstrap}, author = {Adriaan P Hilbers and David J Brayshaw and Axel Gandy}, year = {2019}, eprint = {1912.10326}, archiveprefix = {arXiv}, primaryclass = {stat.AP} }

@article{GandyVeraart:comppoisson, author = {Axel Gandy and Luitgard A. M. Veraart}, title = {Compound {P}oisson Models for Financial Networks}, year = {2020}, journal = {Mathematics and Financial Economics}, doi = {10.1007/s11579-020-00268-9} }

@unpublished{gandy09:FIC, author = {Axel Gandy and Nils Lid Hjort}, title = {Focussed Information Criteria for Semiparametric Linear Hazard Regression}, year = 2009, abstract = { The semiparametric linear hazard regression model introduced by McKeague and Sasieni (1994) is an extension of the linear hazard regression model developed by Aalen (1980). Methods of model selection for this type of model are still underdeveloped. In the process of fitting a semiparametric linear hazard regression model one usually starts with a given set of covariates. For each covariate one has at least the following three choices: allow it to have time-varying effect; allow it to have constant effect over time; or exclude it from the model. In this paper we discuss focused information criteria (FIC) to help with this choice. In the spirit of Claeskens and Hjort (2003), `focused' means that one is interested in one specific quantity, e.g.~the probability of survival of a patient with a certain set of covariates up to a given time. The FIC involves estimating the mean squared error of the estimator of the quantity one is interested in, and the chosen model is the one minimising this estimated mean squared error. The focused model selection machinery is extended to allow for weighted versions, leading to a suitable wFIC method that aims at finding models that lead to good estimates of a given list of parameters, such as survival probabilities for a subset of patients or for a specified region of covariate vectors. In addition to developing model selection criteria, methods associated with averaging across the best models are also discussed. We illustrate these methods of model selection in a real data situation.} }

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