Dr Glen Livingston Jr
Lecturer
School of Information and Physical Sciences (Data Science and Statistics)
- Email:glen.livingstonjr@newcastle.edu.au
- Phone:(02) 4921 6128
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
- CMH
- Forecasting
- MCMC algorithms
- Non-linear multivariate time series
- non-parametric statistics
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 |
Publications
For publications that are currently unpublished or in-press, details are shown in italics.
Book (1 outputs)
Year | Citation | Altmetrics | Link | ||||||||
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2023 |
Rayner JCW, Livingston GC, An Introduction to Cochran Mantel Haenszel Testing and Nonparametric ANOVA (2023) An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA Complete reference for applied statisticians and data analysts that uniquely covers the new statistical ... [more] An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA Complete reference for applied statisticians and data analysts that uniquely covers the new statistical methodologies that enable deeper data analysis An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA provides readers with powerful new statistical methodologies that enable deeper data analysis. The book offers applied statisticians an introduction to the latest topics in nonparametrics. The worked examples with supporting R code provide analysts the tools they need to apply these methods to their own problems. Co-authored by an internationally recognised expert in the field and an early career researcher with broad skills including data analysis and R programming, the book discusses key topics such as: NP ANOVA methodology Cochran-Mantel-Haenszel (CMH) methodology and design Latin squares and balanced incomplete block designs Parametric ANOVA F tests for continuous data Nonparametric rank tests (the Kruskal-Wallis and Friedman tests) CMH MS tests for the nonparametric analysis of categorical response data Applied statisticians and data analysts, as well as students and professors in data analysis, can use this book to gain a complete understanding of the modern statistical methodologies that are allowing for deeper data analysis.
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Journal article (15 outputs)
Year | Citation | Altmetrics | Link | ||||||||
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2024 |
Rayner JCW, Livingston GC, 'Orthogonal contrasts for both balanced and unbalanced designs and both ordered and unordered treatments', Statistica Neerlandica, 78 68-78 (2024) [C1]
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Nova | |||||||||
2023 |
Hands DE, Janse de Jonge XAK, Livingston GC, Borges NR, 'The effect of match location and travel modality on physical performance in A-League association football matches.', J Sports Sci, 41 565-572 (2023) [C1]
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Nova | |||||||||
2023 |
Rayner JCW, Livingston G, 'Relating the Friedman test adjusted for ties, the Cochran-Mantel-Haenszel mean score test and the ANOVA F test', COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 52 4369-4378 (2023) [C1]
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Nova | |||||||||
2023 |
Adams SR, Toohey LA, Drew MK, Smith C, Borges N, Wollin M, et al., 'Epidemiology of time-loss injuries within an Australian male professional football club: A 5-year prospective observational study of 21,343 player hours.', J Sports Sci, 41 2161-2168 (2023) [C1]
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Nova | |||||||||
2023 |
Rayner JCW, Livingston GC, 'Orthonormal F Contrasts for Factors with Ordered Levels in Two-Factor Fixed-Effects ANOVAs', Stats, 6 920-930 [C1]
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Nova | |||||||||
2022 |
Holt K, Delbridge A, Josey L, Dhupelia S, Livingston GC, Waddington G, Boettcher C, 'Subscapularis tendinopathy is highly prevalent in elite swimmer's shoulders: an MRI study', Journal of Science and Medicine in Sport, 25 720-725 (2022) [C1]
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Nova | |||||||||
2022 |
Livingston GCJJ, Nur D, 'Bayesian inference of multivariate-GARCH-BEKK models', STATISTICAL PAPERS, (2022) [C1]
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Nova | |||||||||
2022 |
Livingston GC, Rayner JCW, 'Nonparametric Analysis of Balanced Incomplete Block Rank Data', JOURNAL OF STATISTICAL THEORY AND PRACTICE, 16 (2022) [C1]
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Nova | |||||||||
2022 |
Rayner JCW, Livingston GC, 'Ordinal Cochran-Mantel-Haenszel Testing and Nonparametric Analysis of Variance: Competing Methodologies', Stats, 5 970-976 [C1]
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Nova | |||||||||
2022 |
Livingston G, Allingham D, Rayner JCW, 'Tests for aggregated dispersion: Van Valen's test and a new competitor', ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 29 223-239 (2022) [C1]
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Nova | |||||||||
2020 |
Pearson M, Livingston G, King R, 'An exploration of predictive football modelling', Journal of Quantitative Analysis in Sports, 16 27-39 (2020) [C1]
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Nova | |||||||||
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.
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Nova | |||||||||
2020 |
Livingston G, Nur D, 'Bayesian inference of smooth transition autoregressive (STAR)(k) GARCH(l, m) models', Statistical Papers, 61 2449-2482 (2020) [C1] 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.
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Nova | |||||||||
2019 |
Livingston G, Nur D, 'Bayesian estimation and model selection of a multivariate smooth transition autoregressive model', ENVIRONMETRICS, 31 (2019) [C1]
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Nova | |||||||||
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) [C1]
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Nova | |||||||||
Show 12 more journal articles |
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] |
Research Supervision
Number of supervisions
Current Supervision
Commenced | Level of Study | Research Title | Program | Supervisor Type |
---|---|---|---|---|
2022 | PhD | New Algorithms for Analysing Big Time Series Data: Nexus Between Classical Statistical Models and Modern Data Science Methods | PhD (Statistics), College of Engineering, Science and Environment, The University of Newcastle | Principal Supervisor |
2021 | PhD | Exploring Multifaceted Clustering of Complex Electricity Time-Series Data to Support Data-Driven Decision-Making in the Energy Sector | PhD (Statistics), College of Engineering, Science and Environment, The University of Newcastle | Principal Supervisor |
2020 | PhD | Contextualising Physical Performance in Association Football Matches: Traditional GPS Measures, Tactical Actions and Spatiotemporal Measures. | PhD (Exercise & Sport Science), College of Health, Medicine and Wellbeing, The University of Newcastle | Co-Supervisor |
Past Supervision
Year | Level of Study | Research Title | Program | Supervisor Type |
---|---|---|---|---|
2023 | PhD | Player Monitoring and Secondary Injury Prevention in Male Professional Football | PhD (Exercise & Sport Science), College of Health, Medicine and Wellbeing, The University of Newcastle | Co-Supervisor |
2021 | Honours | A New Algorithm for Fitting ARMA Models to Big Time Series Data | Statistics, School of Mathematical and Physical Sciences, The University of Newcastle | Co-Supervisor |
2021 | Honours | A Comparison of Univariate GARCH Models | Statistics, School of Mathematical and Physical Sciences, The University of Newcastle | Principal Supervisor |
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 |
2020 | Honours | Efficient Rolling Average Algorithm to Estimate ARMA Models for Big Time Series Data | Statistics, School of Mathematical and Physical Sciences, The University of Newcastle | Co-Supervisor |
2019 | Honours | Predictive Modelling of Rugby League Scores | Statistics, School of Mathematical and Physical Sciences, The University of Newcastle | Principal Supervisor |
2018 | Honours | An Exploration of Predictive Football Modelling | Statistics, The University of Newcastle | Co-Supervisor |
Dr Glen Livingston Jr
Position
Lecturer
School of Information and Physical Sciences
College of Engineering, Science and Environment
Focus area
Data Science and Statistics
Contact Details
glen.livingstonjr@newcastle.edu.au | |
Phone | (02) 4921 6128 |
Office
Room | SR272 |
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Building | Social Science (SR) |
Location | Callaghan University Drive Callaghan, NSW 2308 Australia |