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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.

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
• Khan Buchwald: Comparisons Between Functional Brain Networks for Schizophrenia and Healthy Case Controls: A Bayesian Network Approach
• 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
• Bradley Drayton: Variance estimators for mixed effects proportional hazards models fitted to complex samples
• 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
• 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
• Ciprian Doru Giurcaneanu: Dissimilarity measures for time series
• 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.
• 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: 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
• 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 prevalance study
• 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
• 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
• 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
• 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
• Siwei Zhai: Childhood Risk and Resilience Factors for Pasifika Youth Respiratory Health: Accounting for Attrition and Missingness
• Wenqi Zhao: Extension of VGAM package

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