Dr Glen Livingston

Dr Glen Livingston

Lecturer

School of Mathematical 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
010499 Statistics not elsewhere classified 40
010405 Statistical Theory 20
010406 Stochastic Analysis and Modelling 40

Professional Experience

UON Appointment

Title Organisation / Department
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For publications that are currently unpublished or in-press, details are shown in italics.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 (2 outputs)

Year Citation Altmetrics Link
2018 Livingston G, Nur D, 'Bayesian inference of smooth transition autoregressive (STAR)(k) GARCH(l, m) models', Statistical Papers, (2018)

© 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.

DOI 10.1007/s00362-018-1056-3
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 - 2Web of Science - 1
Co-authors Darfiana Nur

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]
Co-authors Darfiana Nur
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Research Supervision

Number of supervisions

Completed1
Current1

Current Supervision

Commenced Level of Study Research Title Program Supervisor Type
2015 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

Past Supervision

Year Level of Study Research Title Program Supervisor Type
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 Mathematical and Physical Sciences
Faculty of Science

Contact Details

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

Office

Room SR-115
Building Social Science (SR)
Location Callaghan
University Drive
Callaghan, NSW 2308
Australia
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