63 citations · 531 across the 74 of their papers we have counts for
30 papers · 1 filter
Bayesian Design for Sampling Anomalous Spatio-Temporal Data
Katie Buchhorn, Kerrie Mengersen, Edgar Santos-Fernandez +1
Data collected from arrays of sensors are essential for informed decision-making in various systems. However, the presence of anomalies can compromise the accuracy and reliability…
Mapping the prevalence of cancer risk factors at the small area level in Australia
James Hogg, Jessica Cameron, Susanna Cramb +2
Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area level data on cancer ri…
Increasing trust in new data sources: crowdsourcing image classification for ecology
Edgar Santos-Fernandez, Julie Vercelloni, Aiden Price +4
Crowdsourcing methods facilitate the production of scientific information by non-experts. This form of citizen science (CS) is becoming a key source of complementary data in many f…
Where are the vulnerable children? Identification and comparison of clusters of young children with health and developmental vulnerabilities across Queensland
Wala Draidi Areed, Aiden Price, Kathryn Arnett +2
This study aimed to better understand the vulnerability of 5 to 6 year old children in their first year of school, based on five health and development domains. Identification of s…
Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Joshua J. Bon, Adam Bretherton, Katie Buchhorn +12
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most sta…
Stateful to Stateless: Modelling Stateless Ethereum
Sandra Johnson, David Hyland-Wood, Anders L Madsen +1
The concept of 'Stateless Ethereum' was conceived with the primary aim of mitigating Ethereum's unbounded state growth. The key facilitator of Stateless Ethereum is through the int…
On the intrinsic dimensionality of Covid-19 data: a global perspective
Abhishek Varghese, Edgar Santos-Fernandez, Francesco Denti +2
This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covi…
Understanding links between water-quality variables and nitrate concentration in freshwater streams using high-frequency sensor data
Claire Kermorvant, Benoit Liquet, Guy Litt +5
Real time monitoring using in situ sensors is becoming a common approach for measuring water quality within watersheds. High frequency measurements produce big data sets that prese…
A Bayesian latent allocation model for clustering compositional data with application to the Great Barrier Reef
Luiza Piancastelli, Nial Friel, Julie Vercelloni +2
Relative abundance is a common metric to estimate the composition of species in ecological surveys reflecting patterns of commonness and rarity of biological assemblages. Measureme…
Spatio-temporal quantile regression analysis revealing more nuanced patterns of climate change: a study of long-term daily temperature in Australia
Qibin Duan, Clare A. McGrory, Glenn Brown +2
Climate change is commonly associated with an overall increase in mean temperature in a defined past time period. Many studies consider temperature trends at the global scale, but…
A statistical machine learning approach for benchmarking in the presence of complex contextual factors and peer groups
Daniel W. Kennedy, Jessica Cameron, Paul P. -Y. Wu +1
The ability to compare between individuals or organisations fairly is important for the development of robust and meaningful quantitative benchmarks. To make fair comparisons, cont…
Peer groups for organisational learning: clustering with practical constraints
Daniel William Kennedy, Jessica Cameron, Paul Pao-Yen Wu +1
Peer-grouping is used in many sectors for organisational learning, policy implementation, and benchmarking. Clustering provides a statistical, data-driven method for constructing m…
Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective
Edgar Santos-Fernandez, Erin E. Peterson, Julie Vercelloni +2
Many research domains use data elicited from "citizen scientists" when a direct measure of a process is expensive or infeasible. However, participants may report incorrect estimate…
Bayesian item response models for citizen science ecological data
Edgar Santos-Fernandez, Kerrie Mengersen
So-called 'citizen science' data elicited from crowds has become increasingly popular in many fields including ecology. However, the quality of this information is being frequently…
The role of intrinsic dimension in high-resolution player tracking data -- Insights in basketball
Edgar Santos-Fernandez, Francesco Denti, Kerrie Mengersen +1
A new range of statistical analysis has emerged in sports after the introduction of the high-resolution player tracking technology, specifically in basketball. However, this high d…
Factors associated with injurious from falls in people with early stage Parkinson's disease
Sarini Abdullah, James McGree, Nicole White +2
Falls are common in people with Parkinson's disease (PD) and have detrimental effects which can lower the quality of life. While studies have been conducted to learn about falling…
Profile regression for subgrouping patients with early stage Parkinson's disease
Sarini Abdullah, James McGree, Nicole White +2
Falls are detrimental to people with Parkinson's Disease (PD) because of the potentially severe consequences to the patients' quality of life. While many studies have attempted to…
Assessing the predictive ability of the UPDRS for falls classification in early stage Parkinson's disease
Sarini Abdullah, Nicole White, James McGree +2
Identification of risk factors associated with falls in people with Parkinson's Disease (PD) is important due to their high risk of falling. In this study, various ways of utilizin…
Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation
Abhishek Varghese, Christopher Drovandi, Kerrie Mengersen +1
The Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to…
A feature-based framework for detecting technical outliers in water-quality data from in situ sensors
Priyanga Dilini Talagala, Rob J. Hyndman, Catherine Leigh +2
Outliers due to technical errors in water-quality data from in situ sensors can reduce data quality and have a direct impact on inference drawn from subsequent data analysis. Howev…
Predicting Sediment and Nutrient Concentrations in Rivers Using High Frequency Water Quality Surrogates
Catherine Leigh, Sevvandi Kandanaarachchi, James M. McGree +4
A particular focus of water-quality monitoring is the concentrations of sediments and nutrients in rivers, constituents that can smother biota and cause eutrophication. However, th…
A framework for automated anomaly detection in high frequency water-quality data from in situ sensors
Catherine Leigh, Omar Alsibai, Rob J. Hyndman +9
River water-quality monitoring is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values. However, anomalies caused by techni…
Monitoring through many eyes: Integrating disparate datasets to improve monitoring of the Great Barrier Reef
Erin E Peterson, Edgar Santos-Fernández, Carla Chen +13
Numerous organisations collect data in the Great Barrier Reef (GBR), but they are rarely analysed together due to different program objectives, methods, and data quality. We develo…
Ultrahigh Dimensional Variable Selection for Mapping Soil Carbon
Benjamin R. Fitzpatrick, David W. Lamb, Kerrie Mengersen
Modern soil mapping is characterised by the need to interpolate samples of geostatistical response observations and the availability of relatively large numbers of environmental ch…