Some of my research interests are:
- Flexible regression models
- Models for longitudinal and clustered data
- Models for count data
- Inference using approximations to the likelihood, e.g. Laplace approximations
You can view my published papers on Google scholar.
I have written two R packages:
- glmmsr, which may be used to fit Generalized Linear Mixed Models, with a choice of which method to use to approximate the likelihood.
- flexl, which may be used to flexibly estimate subject-specific mean curves for simple longitudinal data.
Preprints
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Ogden, H. (2024). Flexible Models for Simple Longitudinal Data. arXiv:2401.11827. [link]
Publications
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Pritchard, Y., Sharma, A., Clarkin, C., Ogden, H., Mahajan, S. & Sánchez-García, R. (2023). Persistent homology analysis distinguishes pathological bone microstructure in non-linear microscopy images. Scientific Reports. 13 (2522). [link]
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Geroldinger, A., Blagus, R., Ogden, H. & Heinze, G. (2022). An investigation of penalization and data augmentation to improve convergence of generalized estimating equations for clustered binary outcomes. BMC Medical Research Methodology. 22 (1). [link]
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Ogden, H. (2021). On the error in Laplace approximations of high-dimensional integrals. Stat. 10(1), e380. [link]
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Böhning, D., & Ogden, H. E. (2021). General flation models for count data. Metrika, 84, 245-261. [link]
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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]