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NZSA 2024 — Contributed Abstracts

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The following presentations have all been accepted for presentation at NZSA 2024, either as a Poster or (more commonly) delivered as a Talk.

Presenters are listed in alphabetical order below, using last names. Clicking on the title of a presentation will open the contributed abstract in a new tab. Any presentations that will be displayed as posters are noted on the individual contributed abstracts.

Contributed presentations are 20 minutes total duration per talk, which includes a couple of minutes for questions.

Full written papers are not required.

• Jessica Allen: Statistical modelling of slow and fast earthquakes in the Hikurangi Subduction Zone, New Zealand
• Andrew Balemi: Teaching GLMS to undergraduates
• Alan Bentley: Introducing the Reserve Bank of New Zealand’s new business expectations survey
• Timothy Bilton: Relatedness estimation in pooled samples sequenced using low-depth high-throughput methods
• Liza Bolton: That's questionable: Designing and deploying effective models for generating multiple versions of auto-marked questions
• Amin Boumerdassi: Investigating the impact of gravitational wave glitches on the parameter estimation of extreme mass ratio inspirals
• Khan Buchwald: Comparisons between functional brain networks for schizophrenia and healthy case controls: A Bayesian network approach
• Lisa Chen: Is AI eating data scientists' lunch?
• Len Cook: Where missing values really matter for models in public policy – real life examples
• Sarah Croft: A Bayesian close-kin mark-recapture model using pedigree reconstruction
• Ying Cui: Semi-supervised model-based clustering for ordinal response data
• James Curran: What the Zeta!
• Tilman M. Davies: A nearest-neighbour Gaussian process spatial factor model for censored, multi-depth geochemical data
• Yongshi Deng: Vismi: Visualisation tools for multiple imputation
• Yongshi Deng: Cluster analysis of unknown samples using Lumi™ drug scan data
• Tori Diamond: Novel applications of linked administrative data – adding longitudinal capability to the Te Kupenga survey
• Rasika Dilhani: Ordinal pattern analysis for early bearing fault detection and classification in rotating machinery
• Bradley Drayton: Variance estimators for mixed effects proportional hazards models fitted to complex samples
• Tarin Eccleston: Variational autoencoders for stellar core-collapse gravitational waves
• Matt Edwards: A novel stacked hybrid autoencoder for imputing data gaps in the Laser Interferometer Space Antenna
• Joane Elleouet: Predicting the size of any species' genome using the taxonomy tree
• Tom Elliott: Introducing rserve-ts: a modernised library for R-to-web communication
• Jamas Enright: Synthetic bridge over troubled data
• Anna Fergusson: Lost (and found) in translation: Examining the diversity and impact of languages selected on student responses to a statistical investigation of automated language translation
• Alejandro Frery: Ordinal Patterns, features and their distribution
• Linus Fromm: The algebra and geometry of Markov bases
• Ciprian Doru Giurcăneanu: Dissimilarity measures for time series
• Adam Glucksman: The subjective wellbeing of first-in-family university students: A multivariate re-evaluation of common narratives
• John Harraway: Motivational resources for training in statistics
• Oxana Hart: Using spatio-temporal models to investigate stock structure, seasonality and environment influences in the snapper population from the west coast of New Zealand
• Martin Hazelton: Model-based priors for network tomography
• Alec van Helsdingen: Extending spatial capture-recapture with the Hawkes process
• Guoping Hu: Forecasting multiple time series with graph convolutional networks
• Charlotte Jones-Todd: A virtual experiment to teach experimental design
• Charlotte Jones-Todd: Accounting for social networks in a self-exciting point process model
• Takafumi Kubota: Interactive visualization of suicide methods in Tokyo Japan
• Thomas Lumley: Speeding up design in Bayesian platform trials
• Bethany Macdonald: Improving minimum contrast for clustered processes
• Qingyu Meng: An explainable disease risk prediction model based on deep transfer learning using high-dimensional genomic data
• Justin Murphy: A tool to evaluate environmental trend estimation methods
• Sajeeka Nanayakkara: Evaluating the predictive performance of regression and machine learning models for rare events: A simulation study on structural recurrence in thyroid cancer
• Amanda O'Connell, James Maguire, Roimata Timutimu: Methodology for Māori descent in the 2023 Census
• Soyoon Annie Park: Investigating the effect of repeated patients in surgical outcomes research
• Priya Parmar: Identifying influential factors impacting trajectories of anxiety in adolescents experiencing adverse events
• PRESENTATION WITHDRAWN Gabriele Frigerio Porta: A statistician in the public sector
• Mario Prado: Navigating the maze of multi-omics methods for classification including microbiome data
• Sam Preston: Understanding the lived experiences of impostor phenomenon among leaders in Mathematics and Statistics
• Matt Pyper: Statistical evaluation of neural network Hawkes processes
• Alice Richardson: Trends in current smoking status at state-age-sex level in Australia: An application of multinomial multilevel time-series modelling
• Claudia Rivera-Rodriguez: Sampling small older populations: Methods and challenges of a dementia prevalence study
• Walter Somerville, James Maguire, Roimata Timutimu: Methodology for iwi affiliation in the 2023 Census
• Janet Stacey: State of the nation: Accessibility of vaping products in New Zealand
• Budhi Arta Surya: Maximum likelihood recursive state estimation
• Rolf Turner: Pseudo analysis of variance of K-functions
• Simon Urbanek: Democratising interactive statistical graphics
• Alain C. Vandal: Reporting randomised factorial trial results: the Topical Analgesia Post-Haemorrhoidectomy trial
• Tieta Vesty, Meenu Jose, Roimata Timutimu: Admin data quality for iwi affiliation in the 2023 Census
• Kang Wang: Galaxy cluster gas pressure profile modelling with reversible jump MCMC: A case study using Planck data
• Mark Weatherall: Validation of clinically important differences for randomised controlled trials in asthma
• Zhijian Wen: An adaptive polynomial baseline correction method in voltammetry with application to the prediction of the concentration of cocaine aptamer
• Ian Westbrooke: What makes for a happy tramper?
• Manori Wickramasinghe: Improving probabilistic linking in the IDI
• Robin Willink: A logical contradiction when considering an unknown constant to have a probability density function
• Robin Willink: A simple adjustment makes the Wilcoxon rank-sum/Mann-Whitney test uniformly more powerful
• Daniel Wrench: Quantifying the effect of data gaps on structure functions of turbulent time series: are the biases remediable?
• Angeline Xiao: Evaluating the impact of timely access to concussion services on patient outcomes in Aotearoa
• Xun Xiao: Learning cascading failure pattern in massive pipe network data: a plumber's guide
• Thomas Yee: Heaping and seeping, GAITD regression and doubly constrained reduced rank vector generalized linear models, in smoking studies
• Hannah Yun: Advanced methods for time series data applied to prediction of operating modes for wind turbines
• PRESENTATION WITHDRAWN Zehua Zang: Branching processes with detection: Probabilistic analysis
• Siwei Zhai: Childhood risk and resilience factors for Pasifika youth respiratory health: Accounting for attrition and missingness
• Hengzhe Zhang: EvoFeat: Genetic programming-based feature engineering approach to tabular data
• Wenqi Zhao: Extension of VGAM package

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