I am interested in methods used to do inference for models where the likelihood function is difficult to compute. Some of my research interests are:
- Inference using approximations to the likelihood, e.g. Laplace approximations
- Inference for latent variable models, e.g. Generalized Linear Mixed Models
- Composite likelihood methods
You can view my published papers on Google scholar.
I have written an R package glmmsr, which may be used to fit Generalized Linear Mixed Models, with a choice of which method to use to approximate the likelihood.
Preprints
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Ogden, H. (2018). On the error in Laplace approximations of high-dimensional integrals. arXiv 1808.06341. [link]
Publications
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Ogden, H. (2017). On asymptotic validity of naive inference with an approximate likelihood. Biometrika, 104(1), 153-164. [link]
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Ogden, H. E. (2016). A caveat on the robustness of composite likelihood estimators: The case of a mis-specified random effect distribution. Statistica Sinica, 26(2), 639-651. [link]
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Ogden, H. E. (2015). A sequential reduction method for inference in generalized linear mixed models. Electronic Journal of Statistics, 9(1), 135-152. [link]
Theses
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Ogden, H. E. (2014). Inference for generalised linear mixed models with sparse structure. PhD thesis, University of Warwick. [link]