Dr Glen Livingston
School of Mathematical and Physical Sciences
- Phone:(02) 4921 6128
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.
- 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
- Bayesian statistics
- Big time series
- MCMC algorithms
- Non-linear multivariate time series
Fields of Research
|010499||Statistics not elsewhere classified||40|
|010406||Stochastic Analysis and Modelling||40|
|Title||Organisation / Department|
For publications that are currently unpublished or in-press, details are shown in italics.
Journal article (5 outputs)
Pearson M, Livingston G, King R, 'An exploration of predictive football modelling', Journal of Quantitative Analysis in Sports, 16 27-39 (2020) [C1]
Rayner JCW, Livingston G, 'The Kruskal Wallis tests are Cochran Mantel Haenszel mean score tests', Metron, (2020) [C1]
© 2020, Sapienza Università di Roma. The Kruskal¿Wallis tests are appropriate tests for the completely randomised design, both for when the data are untied ranks, and, with adjust... [more]
© 2020, Sapienza Università di Roma. 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.
Livingston G, Nur D, 'Bayesian inference of smooth transition autoregressive (STAR)(k) GARCH(l, m) models', Statistical Papers, 61 2449-2482 (2020)
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The smooth transition autoregressive (STAR)(k)¿GARCH(l,¿m) model is a non-linear time series model that is able to a... [more]
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. 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.
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)
|Show 2 more journal articles|
Conference (1 outputs)
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]
Number of supervisions
|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), Faculty of Science, The University of Newcastle||Co-Supervisor|
|2018||Honours||An Exploration of Predictive Football Modelling||Statistics, The University of Newcastle||Co-Supervisor|