Staff Profile

Roles and responsibilities

Internal

• Teaching and Curriculum Coordinator (TCC)/Deputy Director of Statistics
• Personal tutor

External 

• Associate Editor, Computational Statistics and Data Analysis

Memberships

• Fellow of the Royal Statistical Society
• Fellow of the Higher Education Academy

Google Scholar: Click here.

SCOPUS: Click here.

Research Interests

Google Scholar

• Applied statistics: Bayesian modelling and data analysis
• Latent variable models (e.g. HMMs, mixtures, ranks, paired comparisons)
• Computer model emulation and calibration
• Sports analytics (data analysis, modelling, forecasting)

PhD Supervision

• Josh Cowley (2019 - 2024) Real time monitoring of groundwater monitoring networks using telemetry. supervised jointly with Colin Gillespie. Partly funded by Shell. 
• Jack Kennedy (2018 - 2023) Elicitation and prior specification for uncertainty analysis in large complex energy systems models. Supervised jointly with Kevin Wilson.
• Stephen Johnson (2014-2018) Bayesian modelling and analysis of ranked data. Supervised jointly with Richard Boys.

Undergraduate Modules (2023/24)

• PSY2010 Statistics for Empirical Psychology (Semester 1)
• MAS2902 Introduction to Regression & Stochastic Modelling (Semester 2) 
• MAS3918/MAS8918 Topics in Statistical Modelling A (Semester 2)
• NUT3002 Research Project (Semester 2)
• MAS8391 MMathStat Project

• Articles

  • Kennedy JC, Henderson DA, Wilson KJ. Multilevel Emulation for Stochastic Computer Models with Application to Large Offshore Wind Farms. Journal of the Royal Statistical Society, Series C: Applied Statistics 2023, Epub ahead of print.
  • Johnson SR, Henderson DA, Boys RJ. On Bayesian inference for the Extended Plackett-Luce model. Bayesian Analysis 2022, 17(2), 465-490.
  • Johnson SR, Henderson DA, Boys RJ. Revealing Subgroup Structure in Ranked Data Using a Bayesian WAND. Journal of the American Statistical Association 2019, 115(532), 1888-1901.
  • Wilson KJ, Henderson DA, Quigley J. Emulation of utility functions over a set of permutations: sequencing reliability growth tasks. Technometrics 2018, 60(3), 273-285.
  • Henderson DA, Kirrane LJ. A comparison of truncated and time-weighted Plackett-Luce models for probabilistic forecasting of Formula One results. Bayesian Analysis 2018, 13(2), 335-358.
  • Sherlock C, Golightly A, Henderson DA. Adaptive, delayed-acceptance MCMC for targets with expensive likelihoods. Journal of Computational and Graphical Statistics 2016, 26(2), 434-444.
  • Golightly A, Henderson DA, Sherlock C. Delayed acceptance particle MCMC for exact inference in stochastic kinetic models. Statistics and Computing 2015, 25(5), 1039-1055.
  • Henderson DA, Baggaley AW, Shukurov A, Boys RJ, Sarson GR, Golightly A. Regional variations in the European Neolithic dispersal: the role of the coastlines. Antiquity 2014, 88(342), 1291-1302.
  • Henderson DA, Boys RJ, Wilkinson DJ. Bayesian Calibration of a Stochastic Kinetic Computer Model Using Multiple Data Sources. Biometrics 2010, 66(1), 249-256.
  • Jones MC, Henderson DA. Maximum likelihood kernel density estimation: on the potential of convolution sieves. Computational Statistics and Data Analysis 2009, 53(10), 3726-3733.
  • Henderson DA, Boys RJ, Krishnan KJ, Lawless C, Wilkinson DJ. Bayesian Emulation and Calibration of a Stochastic Computer Model of Mitochondrial DNA Deletions in Substantia Nigra Neurons. Journal of the American Statistical Association 2009, 104(485), 76-87.
  • Jones MC, Henderson DA. Miscellanea kernel-type density estimation on the unit interval. Biometrika 2007, 94(4), 977-984.
  • Boys RJ, Henderson DA. Rejoinder to "Discussion of "A Bayesian approach to DNA segmentation by R. J. Boys and D. A. Henderson" by Booth, Burden, Maindonald, Santoso, Wakefield and Wilson". Biometrics 2005, 61(2), 635-639.
  • Boys RJ, Henderson DA. A Bayesian Approach to DNA Sequence Segmentation. Biometrics 2004, 60(3), 573-581.
  • Samuels DC, Boys RJ, Henderson DA, Chinnery PF. A compositional segmentation of the human mitochondrial genome is related to heterogeneities in the guanine mutation rate. Nucleic Acids Research 2003, 31(20), 6043-6052.
  • Boys RJ, Henderson DA. On determining the order of Markov dependence of an observed process governed by a hidden Markov model. Scientific Programming 2002, 10(3), 241-251.
  • Boys RJ, Henderson DA, Wilkinson DJ. Detecting homogeneous segments in DNA sequences by using hidden Markov models. Journal of the Royal Statistical Society. Series C: Applied Statistics 2000, 49(2), 269-285.
  • Avery PJ, Henderson DA. Fitting Markov chain models to discrete state series such as DNA sequences. Journal of the Royal Statistical Society. Series C: Applied Statistics 1999, 48(1), 53-61.
  • Avery PJ, Henderson DA. Detecting a changed segment in DNA sequences. Journal of the Royal Statistical Society. Series C: Applied Statistics 1999, 48(4), 489-503.

• Book Chapter

  • Henderson DA, Boys RJ, Proctor CJ, Wilkinson DJ. Linking systems biology models to data: a stochastic kinetic model of p53 oscillations. In: A. O'Hagan and M. West, ed. The Oxford Handbook of Applied Bayesian Analysis. Oxford University Press, 2010, pp.155-187.

• Conference Proceedings (inc. Abstract)

  • Boys RJ, Henderson DA. A comparison of reversible jump MCMC algorithms for DNA sequence segmentation using hidden Markov models. In: Frontiers in data mining and bioformatics : 33rd Symposium on the Interface. 2001, Costa Mesa, Orange County, California, USA: Interface Foundation of North America.

• Note

  • Boys RJ, Henderson DA. Discussion of "A Bayesian approach to DNA sequence segmentation": Commentary. Biometrics 2005, 61(2), 637-639.