preprints.bib

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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@unpublished{gandy09:FIC,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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