Haziq Jamil

Haziq Jamil

Assistant Professor in Statistics


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About

The contents of this website are based on the PhD thesis entitled Regression modelling using priors depending on Fisher information covariance kernels (I-priors) by Haziq Jamil. It is intended as an accompaniment to the research poster of the same title.

This project was supervised by Wicher Bergsma and Irini Moustaki.

Author

Haziq Jamil is currently an Assistant Professor in Statistics at Universiti Brunei Darussalam (UBD). His interests are in statistical methodology and computation, with an emphasis on applications towards the social sciences.

Haziq graduated with first class honours from The University of Warwick in 2010, where he read Mathematics, Operational Research, Statistics and Economics. He then obtained an MSc in Statistics with distinction at the London School of Economics and Political Science in 2014. He completed his PhD in Statistics from the same university in 2018.

Previously, Haziq worked as a research officer at the Centre of Science and Technology, Research and Development at the Ministry of Defence, Brunei, where he provided scientific decision support for strategic acquisition projects.

References

Introduction

  • Bergsma, W. (2019). Regression with I-priors. Econometrics and Statistics. DOI: 10.1016/j.ecosta.2019.10.002.
  • Berlinet, A., & Thomas-Agnan, C. (2011). Reproducing Kernel Hilbert Spaces in Probability and Statistics. Springer Science & Business Media. DOI: 10.1007/978-1-4419-9096-9.
  • Neal, R. M. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo, 2(11). Chapman & Hall/CRC Press. arXiv: 1206.1901v1.
  • Ong, C. S., Mary, X., Canu, S., & Smola, A. J. (2004, July). Learning with non-positive kernels. In Proceedings of the Twenty-first International Conference on Machine Learning (p. 81). ACM. DOI: 10.1145/1015330.1015443 .
  • Steinwart, I., & Christmann, A. (2008). Support Vector Machines. Springer. 10.1007/978-0-387-77242-4

Regression

  • Rasmussen, C. E., & Williams, C. K. (2006). Gaussian Processes for Machine Learning. The MIT press. ISBN: 0-262-18253-X.
  • Williams, C. K., & Seeger, M. (2001). Using the Nyström method to speed up kernel machines. In Advances in Neural Information Processing Systems 13, 682-688. MIT Press. URL.

Classification

  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. ISBN: 978-0-387-31073-2.
  • Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational Inference: A Review for Statisticians. Journal of the American Statistical Association, to appear. arXiv: 1601.00670.
  • Girolami, M., & Rogers, S. (2006). Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. Neural Computation, 18(8), 1790-1817. DOI: 10.1162/neco.2006.18.8.1790.

Variable Selection

  • Kuo, L., & Mallick, B. (1998). Variable selection for regression models. Sankhyā: The Indian Journal of Statistics, Series B, 65-81. URL.
  • O’Hara, R. B., & Sillanpää, M. J. (2009). A review of Bayesian variable selection methods: what, how and which. Bayesian analysis, 4(1), 85-117. DOI: 10.1214/09-BA403.

Examples

  • Cannings, T. I., & Samworth, R. J. (2017). Random‐projection ensemble classification. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(4), 959-1035. DOI: 10.1111/rssb.12228.
  • Diggle, P., Zheng, P., & Durr, P. (2005). Nonparametric estimation of spatial segregation in a multivariate point process: bovine tuberculosis in Cornwall, UK. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(3), 645-658. DOI: 10.1111/j.1467-9876.2005.05373.x
  • Goldstein, H., Rasbash, J., Yang, M., Woodhouse, G., Pan, H., Nuttall, D., & Thomas, S. (1993). A Multilevel Analysis of School Examination Results. Oxford Review of Education, 19(4), 425-433. DOI: 10.1080/0305498930190401.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer. DOI: 10.1007/978-0-387-84858-7.
  • Kenward, M. G. (1987). A Method for Comparing Profiles of Repeated Measurements. Journal of the Royal Statistical Society: Series C (Applied Statistics), 36(3), 296-308. DOI: 10.2307/2347788.
  • Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman & Hall/CRC. ISBN: 978-1-58488-000-4.

Copyright © 2014-present by Haziq Jamil. All rights reserved. The contents of this website or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the author.

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