Dr Glen Livingston

Dr Glen Livingston

Lecturer

School of Information and Physical Sciences

Career Summary

Biography

Dr Glen Livingston Jr is a lecturer in statistics at The University of Newcastle with a background in multivariate time series modelling, regime switching volatility models, simulation and computational statistics, and Bayesian estimation methods including Markov chain Monte Carlo. Glen has been involved with demand forecasting research projects for the Nestlé Company as well as several other industry partners. He has also conducted research in sports analytics, nutrition and food science, as well as statistical methodology in a range of fields.


Qualifications

  • Doctor of Philosophy in Statistics, University of Newcastle
  • Bachelor of Commerce, University of Newcastle
  • Graduate Diploma, Institute of Chartered Accountants - Australia
  • Bachelor of Mathematics (Honours), University of Newcastle

Keywords

  • Bayesian statistics
  • Big time series
  • Forecasting
  • MCMC algorithms
  • Non-linear multivariate time series

Fields of Research

Code Description Percentage
490509 Statistical theory 20
490599 Statistics not elsewhere classified 40
490510 Stochastic analysis and modelling 40

Professional Experience

UON Appointment

Title Organisation / Department
Lecturer University of Newcastle
School of Information and Physical Sciences
Australia
Lecturer University of Newcastle
School of Mathematical and Physical Sciences
Australia
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Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Journal article (5 outputs)

Year Citation Altmetrics Link
2020 Pearson M, Livingston G, King R, 'An exploration of predictive football modelling', Journal of Quantitative Analysis in Sports, 16 27-39 (2020) [C1]
DOI 10.1515/jqas-2019-0075
Co-authors Robert King
2020 Rayner JCW, Livingston G, 'The Kruskal Wallis tests are Cochran Mantel Haenszel mean score tests', Metron, 78 353-360 (2020) [C1]

The Kruskal¿Wallis tests are appropriate tests for the completely randomised design, both for when the data are untied ranks, and, with adjustment, for when there are ties and mid... [more]

The Kruskal¿Wallis tests are appropriate tests for the completely randomised design, both for when the data are untied ranks, and, with adjustment, for when there are ties and mid-ranks are used. Both these tests are shown to be Cochran¿Mantel¿Haenszel mean score tests. The relationship between the Kruskal¿Wallis test statistic and the ANOVA F test statistic when there are no ties generalises to the same relationship between the Cochran¿Mantel¿Haenszel mean score test statistic and the ANOVA F test statistic. It thus also relates both Kruskal¿Wallis test statistics to the ANOVA F test statistic. A small simulation study finds that p-values may be more accurately found using the F test.

DOI 10.1007/s40300-020-00192-4
Co-authors John Rayner
2020 Livingston G, Nur D, 'Bayesian inference of smooth transition autoregressive (STAR)(k) GARCH(l, m) models', Statistical Papers, 61 2449-2482 (2020)

The smooth transition autoregressive (STAR)(k)¿GARCH(l,¿m) model is a non-linear time series model that is able to account for changes in both regime and volatility respectively. ... [more]

The smooth transition autoregressive (STAR)(k)¿GARCH(l,¿m) model is a non-linear time series model that is able to account for changes in both regime and volatility respectively. The model can be widely applied to analyse the dynamic behaviour of data exhibiting these two phenomenons in areas such as finance, hydrology and climate change. The main aim of this paper is to perform a Bayesian analysis of STAR(k)¿GARCH(l,¿m) models. The estimation procedure will include estimation of the mean and variance coefficient parameters, the parameters of the transition function, as well as the model orders (k,¿l,¿m). To achieve this aim, the joint posterior distribution of the model orders, coefficient and implicit parameters in the logistic STAR(k)¿GARCH(l,¿m) model is presented. The conditional posterior distributions are then derived, followed by the design of a posterior simulator using a combination of MCMC algorithms which includes Metropolis¿Hastings, Gibbs Sampler and Reversible Jump MCMC algorithms. Following this are extensive simulation studies and a case study presenting the methodology.

DOI 10.1007/s00362-018-1056-3
Citations Web of Science - 1
2019 Livingston G, Nur D, 'Bayesian estimation and model selection of a multivariate smooth transition autoregressive model', ENVIRONMETRICS, 31 (2019)
DOI 10.1002/env.2615
Citations Scopus - 1
2017 Livingston G, Nur D, 'Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis', COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 46 5440-5461 (2017)
DOI 10.1080/03610918.2016.1161794
Citations Scopus - 5Web of Science - 4
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Conference (1 outputs)

Year Citation Altmetrics Link
2013 Livingston G, Nur D, Hudson IL, 'A fully Bayesian analysis of Smooth Threshold Autoregressive (STAR) model: A prior sensitivity analysis', Proceedings of International Society for Bayesian Analysis, Section on Economics, Finance and Business (EFaB@Bayes250), Duke University, USA (2013) [E3]
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Research Supervision

Number of supervisions

Completed2
Current0

Past Supervision

Year Level of Study Research Title Program Supervisor Type
2020 PhD Exploring Value-Added Models for American Higher Education Institutions and Omani Post-Basic Education PhD (Statistics), College of Engineering, Science and Environment, The University of Newcastle Co-Supervisor
2018 Honours An Exploration of Predictive Football Modelling Statistics, The University of Newcastle Co-Supervisor
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Dr Glen Livingston

Position

Lecturer
School of Information and Physical Sciences
College of Engineering, Science and Environment

Contact Details

Email glen.livingstonjr@newcastle.edu.au
Phone (02) 4921 6128

Office

Room SR115
Building Social Science (SR)
Location Callaghan
University Drive
Callaghan, NSW 2308
Australia
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