Dr Katharina Parry staff profile picture

Contact details +64 (06) 356 9099  ext. 84613

Dr Katharina Parry PhD, MSc

Lecturer in Statistics

School of Fundamental Sciences

I am a statistician with a background predominately in the field of transportation science, as well as the field of applied multivariate statistics.

Originally, I studied Statistik at Dortmund Universität (Germany) before doing my Masters in Statistics at the University of Auckland (2009), and then my PhD in Statistics at Massey Univeristy (2013). After completing my doctoral work, I joined Auckland University of Technology from 2012 to 2016 as a lecturer in the Mathematical and Computation Sciences. I then returned to Massey University as a lecturer in Statistics in 2016.

Professional

Qualifications

  • Doctor of Philosophy - Massey University (2013)
  • Master of Science (First Class Honours) - University of Auckland (2009)

Research Expertise

Research Interests

My research in transportation studies naturally fall into three interwoven themes.

The first involves developing methods of statistical inference that address the issue of indeterminacy. This is a fundamental problem in transportation because the data available, traffic link counts, does not uniquely describe the ways in which people travel through a system.  My work therefore focuses on a complex class of statistical inverse problems.  A technique using a Bayesian MCMC framework was only available for very simple linear networks, such as one-way bus routes. I expanded the technique to apply to larger networks with bifurcations.

My second research theme relates to how existing transportation models struggle to recreate the kind of day-to-day dynamics we observe in real-life traffic flows. I developed a model that includes additional temporal variation in a parsimonious manner by redefining some of the fixed variables as random variables.  This new class of doubly-stochastic process model, is able to produce simulated flows that display the kind of non-stationary behaviour that we observe in real data.

Finally, these models are very complex and the general question arises on how to well simple data such as link counts function as a means of making inference. Being able to compare models to each other within the Bayesian framework using DIC has allowed me to deliver evidence, of the usefulness of link counts in terms of finding the correct underlying model that lead to the observed data.

More recently, I have been venturing into the field of applied multivariate statistics. My focus lies in the visualisation of relationships between variables in high-dimensional data. I have given conference talks about the impact of data preparation on the possible results of dimension reduction methods and, most recently, an application of my work on avian community data gathered at bird feeders in New Zealand contributed to a journal publication.

Area of Expertise

Field of research codes
Applied Statistics (010401): Biostatistics (010402): Mathematical Sciences (010000): Statistics (010400): Stochastic Analysis and Modelling (010406)

Keywords

Statistical modelling and inference for traffic networks

Visualisations and measures of association for multivariate data.

Research Outputs

Journal

Parry, K., & Vignes, M. (2018). Introduction to High-Dimensional Statistics. BIOMETRICS. 74(4), 1524-1525
[Journal article]Authored by: Parry, K., Vignes, M.
Galbraith, JA., Jones, DN., Beggs, JR., Parry, K., & Stanley, MC. (2017). Urban bird feeders dominated by a few species and individuals. Frontiers in Ecology and Evolution. 5(AUG)
[Journal article]Authored by: Parry, K.
Parry, K., Watling, DP., & Hazelton, ML. (2016). A new class of doubly stochastic day-to-day dynamic traffic assignment models. EURO Journal on Transportation and Logistics. 5(1), 5-23
[Journal article]Authored by: Hazelton, M., Parry, K.
Hazelton, ML., & Parry, K. (2016). Statistical methods for comparison of day-to-day traffic models. Transportation Research Part B: Methodological. 92, 22-34
[Journal article]Authored by: Hazelton, M., Parry, K.
Parry, K., & Hazelton, ML. (2013). Bayesian inference for day-to-day dynamic traffic models. Transportation Research Part B: Methodological. 50, 104-115
[Journal article]Authored by: Hazelton, M., Parry, K.
Parry, K., & Hazelton, ML. (2012). Estimation of origin-destination matrices from link counts and sporadic routing data. Transportation Research Part B: Methodological. 46(1), 175-188
[Journal article]Authored by: Hazelton, M., Parry, K.

Thesis

Parry, K. (2013). Statistical modelling and inference for traffic networks. (Doctoral Thesis, Massey University, New Zealand)
[Doctoral Thesis]Authored by: Parry, K.

Conference

Parry, K., & Tegge, F. (2018, November). What's in a full stop? A caveat of CohMetrix.. Presented at NZSA 2018. Manawatu campus, Massey University.
[Conference Oral Presentation]Authored by: Parry, K.
Parry, K. (2016, November). Should we give a damn? The role of data trans- formation/distance used in analysing count data. Presented at 2016 Joint NZSA/ORSNZ Conference. Auckland, New Zealand.
[Conference Oral Presentation]Authored by: Parry, K.
Parry, K., & Hazelton, ML. (2015, December). How far can you go?. Presented at 2015 AUT Mathematical Sciences Symposium. Auckland, New Zealand.
[Conference Oral Presentation]Authored by: Parry, K.
Parry, K. (2014, November). Bayesian fitting procedures for hydrological point processes. Presented at 2014 Joint NZSA + ORSNZ Conference. Wellington.
[Conference Oral Presentation]Authored by: Parry, K.

Other

Tegge, F., & Parry, K. (2018, October). Tool sharing: What does the text-analysis software Coh-Metrix have to offer?. In School of Psychology, Massey University.
[Oral Presentation]Authored by: Parry, K.

Consultancy and Languages

Languages

  • English
    Last used: Present
    Spoken ability: Excellent
    Written ability: Excellent
  • German
    Last used: Present
    Spoken ability: Excellent
    Written ability: Excellent

Supervision and Teaching

Summary of Doctoral Supervision

Position Current Completed
Co-supervisor 2 0

Courses Coordinated

Current Doctoral Supervision

Co-supervisor of:

  • Ghazaleh Aslani - Doctor of Philosophy
    Traffic Modelling and Inference Using Bluetooth Tracking Data
  • Ahmad Mahmoodjanlou - Doctor of Philosophy
    Modelling and Inference for Dynamic Traffic Networks

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