Dr Matthew Parry

Office: Science III, room 236
Phone: +64 3 479 7780
Email: mparry@maths.otago.ac.nz

** Course coordinator for BAppSc in Data Science **

Research interests

Broadly speaking, my two main interests are in statistical modelling and theoretical statistics. To give you some flavour of what I do:

1. I work on scoring rules which are principled ways of assessing probabilistic statements, e.g. forecasting. Scoring rules have important practical application as well as leading into some very nice mathematics.
2. I am working in plant epidemiology and on sensors for agritech applications. The crucial ingredient in both cases is serious modelling of the underlying physical and biological processes. Inference is then carried via MCMC and particle filter tecnhiques.
3. I also apply statistical modelling to problems in epigenetics and astrophysics.

I am actively looking for Honours and postgraduate students to work on projects in these areas. I value mathematical and computational ability as much as I do statistical background.

Recent Publications

Digital contact tracing technologies in epidemics: a rapid review, Anglemyer A, Moore THM, Parker L, Chambers T, Grady A, Chiu K, Parry M, Wilczynska M, Flemyng E, Bero L., Cochrane Database of Systematic Reviews 2020, Issue 8. Art. No.: CD013699

Extensive Inter-Cyst DNA Methylation Variation in Autosomal Dominant Polycystic Kidney Disease Revealed by Genome Scale Sequencing, S. A. Bowden, P. A. Stockwell, E. J. Rodger, M. F. Parry, M. R. Eccles, C. Stayner, A. Chatterjee, Frontiers in Genetics, 11 (2020) 348

Making sense of the divergent series for reconstructing a Hamiltonian from its eigenstates and eigenvalues, C. M. Bender, D. C. Brody, M.F. Parry, American Journal of Physics, 88 (2) (2020) 148-152

Predicting the periodic risk of anthrax in livestock in Victoria, Australia, using meteorological data, T. Brownlie, T. Bishop, M. Parry, S. E. Salmon, J. C. Hunnam, Int J Biometeorology, 64 (2020) 601-610

Estimating overdispersion in sparse multinomial data, F. Afroz, M. Parry, D. Fletcher, Biometrics, (2019) 1-9

Global DNA methylation levels regulate PD-L1 expression in melanoma, A. Chatterjee, A. Ahn, E. J. Rodger, P. A. Stockwell, M. Parry, J. Motwani, S. J. Gallagher, E. Shklovskaya, J.Tiffen, P.Hersey, M. R. Eccles, Cancer Research, 79 (13 Supplement) (2019) 826-826

Review: “Introduction to Bayesian Statistics” by William M. Bolstad and James M. Curran, M. Parry, Australian & New Zealand Journal of Statistics, 61 (2) (2019) 271-271

‘Do not attempt CPR’ in the community: the experience of ambulance clinicians, S. Moffat, Z. Fritz, A.-M. Slowther, M. Parry, S. Barclay, Journal of Paramedic Practice, 11, 5 (2019) 198-204

Genotype by environment interactions in fertility traits in New Zealand dairy cows, H.J.B. Craig, K. Stachowicz, M. Black, M. Parry, C.R. Burke, S. Meier, & P.R. Amer, Journal of Dairy Science, 101 (2018) 1-3

Marked global DNA hypomethylation is associated with constitutive PD-L1 expression in melanoma, A. Chatterjee, E. J. Rodger, A. Ahn, P. A. Stockwell, M. Parry, J. Motwani, S. J. Gallagher, E. Shklovskaya, J.Tiffen, M. R. Eccles & P. Hersey, iScience, 4 (2018) 312-325

Using niche conservatism information to prioritize hotspots of invasion by non-native freshwater invertebrates in New Zealand, Ursula Torres, William Godsoe, Hannah L. Buckley, Matthew Parry, Audrey Lustig, Susan P. Worner, Diversities and Distributions, 24 (2018) 1802-1815

Grower and regulator conflict in management of the citrus disease Huanglongbing in Brazil: a modelling study, A. P. Craig, N. J. Cunniffe, M. Parry, F. F. Laranjeira & C. A. Gilligan, Journal of Applied Ecology, 55 (4) (2018) 1956-1965

More than Just Numbers: Challenges for Professional Statisticians, C. Cameron, E. Iosua, M. Parry, R. Richards & C. Jaye, Statistics Education Research Journal, 16(2) (2017) 362-375

Comparative assessment of DNA methylation patterns between reduced representation bisulphite sequencing and Sequenom EpiTyper methylation analysis, A. Chatterjee, E. Macaulay, A. Ahn, J. Ludgate, P. Stockwell, R. Weeks, M. Parry, T. Foster, I. Knarston, M. Eccles & I. Morison, Epigenomics, 9 (6) (2017) 823-832

Self-organizing maps for analysing pest profiles: Sensitivity analysis of weights and ranks, M. Roigé, M. Parry, C. Philips & S. Worner, Ecological Modelling, 342 (2016) 113-122

Network rewiring dynamics with convergence towards a star network, P. A. Whigham, G. Dick & M. Parry, Proc. R. Soc. A, 472 (2016) 20160236

scan_tcga tools for integrated epigenomic and transcriptomic analysis of tumor subgroups, A. Chatterjee, P.A. Stockwell, E. J. Rodger, M. F. Parry, & M. R. Eccles, Epigenomics, 8 (10) (2016) 1315-1330

Linear scoring rules for probabilistic binary classification, M. Parry, Electronic Journal of Statistics, Vol. 10, No. 1, (2016) 1596-1607

Efficient recycled algorithms for quantitative trait models on phylogenies, G. Hiscott, C. Fox, M. Parry & D. Bryant, Genome Biology and Evolution Vol. 8, Iss. 5 (2016) 1338-1350

Extensive Scoring Rules, M. Parry, Electronic Journal of Statistics, Vol. 10, No. 1 (2016) 1098-1108

Genome-wide DNA methylation map of human neutrophils reveals widespread inter-individual epigenetic variation, A. Chatterjee, P. Stockwell, E. Rodger, E. Duncan, M. Parry, R. Weeks, I. Morison, Scientific Reports 5 (2015) 17328

Measuring the performance of sensors that report uncertainty, A. Martin. T. Molteno, M. Parry, Proc. Electronics New Zealand Conference (2014)

Bayesian inference for an emerging arboreal epidemic in the presence of control M. Parry, G. Gibson, T. Gottwald, M. Irey, T. Gast, and C. Gilligan, Proc. Natl. Acad. Sci. USA 111, 17 (2014) 6258-6262

Proper local scoring rules, M. Parry, A. P. Dawid, and S. Lauritzen, Ann. Statist. 40, 1 (2012), 561-592

Proper local scoring rules on discrete sample spaces, A. P. Dawid, S. Lauritzen, and M. Parry, Ann. Statist. 40, 1 (2012), 593-608

Informational inefficiency in financial markets, D. C. Brody, B. K. Meister, and M. F. Parry, Math Finan Econ (2012) 6:249–259

Teaching

• COMO 101 Modelling and Computation
• STAT 110 Statistical Methods
• STAT 261 Probability and Inference I
• STAT 312 Modelling High Dimensional Data
• STAT 341 Generalized Linear Models
• STAT 372 Stochastic Modelling
• INFO 420 Statistical Techniques for Data Science
• STAT 442 Big Data
• STAT 444 Stochastic Processes
• MATH 4SL Data Mining, Inference and Prediction