Background

I am a Lecturer in Statistics. I gained a PhD in Bayesian Statistics from Newcastle University in 2011 and prior to that an MMathStat also from Newcastle University in 2007. I joined the University of Strathclyde in May 2011 as a Research Associate in Risk and Reliability and moved to a position of lecturer before leaving the university in August 2015.

My research interests are in the area of Bayesian analysis with focusses on dependence modelling, Bayes linear methods and the elicitation of prior information for subjective inference. My main application areas are risk and reliability analysis and diagnostic test development.

I have published in leading journals in statistics including Bayesian Analysis and Technometrics and risk and reliability including Risk Analysis and Reliability Engineering and System Safety. I have also been involved in industrial projects with organisations including ALSTOM Power, Glasgow Warriors Rugby Club, Northern Gas Networks, Mologic and Network Rail.

I am a fellow of the Royal Statistical Society. I have reviewed papers for the European Journal of Operational Research, Annals of Applied Statistics and Bayesian Analysis amongst others. I am co-investigator on the EPSRC-funded UK Centre for Energy Systems Integration and the NERC-funded Flood-PREPARED project.

I am a member of the School's Business and Engagement Committee and am a personal tutor. 

I enjoy running and compete in road and cross country races for Wallsend Harriers. I don't like egg whites.

Google Scholar: Click here.

Research

Research Interests

• Prior elicitation
• Bayesian design of experiments
• Vine copulas
• Risk and reliability analysis
• Diagnostic test design and analysis
• Bayes linear methods

Full text versions of many of my publications can be found on Newcastle University ePrints.

Current Research Funding

• Co-Investigator, UK Centre for Energy Systems Integration (EPSRC)
• Co-investigator, Flood-PREPARED: Predicting Rainfall Events by Physical Analytics of REaltime Data (NERC)

Videos of Research Talks

Uncertainty elicitation and quantification from experts, Isaac Newton Institute Workshop on Expert Judgement (10th June 2020) 

Teaching

I  2019/20 I am teaching

• Statistics for Experimental Psychology (PSY2010) 
• Introduction to Statistical Inference (MAS2901)
• Bayesian Methodology (MAS8384)

I have also taught

• Multivariate Data Analysis (MAS3325)
• Professional Skills for Accounting and Finance (ACC1012/1053)

Publications

• Umemneku Chikere CM, Wilson K, Graziadio S, Vale L, Allen AJ. Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard – An update. PLoS ONE 2019, 14(10), e0223832.
• Heaps SE, Farrow M, Wilson KJ. Identifying the effect of public holidays on daily demand for gas. Journal of the Royal Statistical Society: Series A 2019, 183(2), 471-492.
• Wilson KJ, Farrow M. Assurance for sample size determination in binomial reliability demonstration testing. In: 10th IMA International Conference on Modelling in Industrial Maintenance and Reliability. 2018.
• Wilson KJ, Farrow M. Combining judgements from correlated experts. In: Luis C. Dias, Alec Morton, John Quigley, ed. Elicitation: the Science and Art of Structuring Judgement. New York: Springer, 2018, pp.211-240.
• 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.
• Wilson KJ. Specification of Informative Prior Distributions for Multinomial Models Using Vine Copulas. Bayesian Analysis 2018, 13(3), 749-766.
• Wilson KJ. An investigation of dependence in expert judgement studies with multiple experts. International Journal of Forecasting 2017, 33(1), 325–336.
• Wilson KJ, Farrow M. Bayes linear kinematics in a dynamic survival model. International Journal of Approximate Reasoning 2017, 80, 239-256.
• Wilson KJ, Quigley J. Allocation of tasks for reliability growth using multi-attribute utility. European Journal of Operational Research 2016, 255(1), 259-271.
• Bedford T, Daneshkhah A, Wilson KJ. Approximate Uncertainty Modeling in Risk Analysis with Vine Copulas. Risk Analysis 2016, 36(4), 792-815.
• Wilson KJ, Heaps SE, Farrow M. Demand forecasting over complex geographical networks: the case of Northern Gas Networks. In: Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016. 2016, Glasgow: CRC Press.
• Revie M, Wilson KJ, Holdsworth R, Yule S. On modeling player fitness in training for team sports with application to professional rugby. International Journal of Sports Science and Coaching 2016, 12(2), 183-193.
• Catterson VM, McMillan D, Dinwoodie I, Revie M, Quigley J, Wilson KJ. An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access. Wind Energy 2015, 19(2), 199-212.
• Cooke RM, Kurowicka D, Wilson K. Sampling, conditionalizing, counting, merging, searching regular vines. Journal of Multivariate Analysis 2015, 138, 4-18.
• Bedford T, Wilson KJ, Daneshkhah A. Assessing parameter uncertainty on coupled models using minimum information methods. Reliability Engineering and System Safety 2014, 125, 3-12.
• Bedford T, Wilson KJ. On the construction of minimum information bivariate copula families. Annals of the Institute of Statistical Mathematics 2014, 66(4), 703-723.
• Ganguly T, Wilson KJ, Quigley J, Cooke RM. Reaction to ‘An approach to perform expert elicitation for engineering design risk analysis: methodology and experimental results’. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2014, 177(4), 981-985.
• Quigley J, Wilson KJ, Walls L, Bedford T. A Bayes linear Bayes method for estimation of correlated event rates. Risk Analysis 2013, 33(12), 2209-2224.
• Wilson KJ, Quigley J, Walls L, Bedford T. Bayes linear Bayes graphical models in the design of optimal test strategies. International Journal of Performability Engineering 2013, 9(6), 715-728.
• Wilson KJ, Farrow M. Bayes linear kinematics in the analysis of failure rates and failure time distributions. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2010, 224(4), 309-321.