Discovery Early Career Researcher Award - Grant ID: DE240100635
Funder
Australian Research Council
Funding Amount
$448,801.00
Summary
Understanding the development of lifestyle behaviours in early childhood. This project adopts novel statistical modelling and machine learning approaches to understand the development of lifestyle behaviours in early childhood. Despite the pivotal role of lifestyle behaviours in influencing health and quality of life, little research exists on lifestyle behaviours in early childhood. This project will establish a comprehensive understanding of lifestyle behaviours in early childhood by identifyi ....Understanding the development of lifestyle behaviours in early childhood. This project adopts novel statistical modelling and machine learning approaches to understand the development of lifestyle behaviours in early childhood. Despite the pivotal role of lifestyle behaviours in influencing health and quality of life, little research exists on lifestyle behaviours in early childhood. This project will establish a comprehensive understanding of lifestyle behaviours in early childhood by identifying key developmental time points, mechanisms of behavioural change, and children at risk of developing poor lifestyle behaviours. The project will inform strategies and policies to optimise lifestyle behaviours from the start of life and showcase the capabilities of novel methods in advancing behavioural epidemiology.Read moreRead less
Mitigating bias in statistical analyses of data collected over time. This project aims to develop innovative nonparametric distribution and regression curve estimation techniques from data collected over time. These curves are key statistical tools for describing populations, but often, their estimators are inefficient when the data are massive, growing and change over time, or too restrictive when the data exhibit measurement errors and a fraction of them are equal to zero. The project expects ....Mitigating bias in statistical analyses of data collected over time. This project aims to develop innovative nonparametric distribution and regression curve estimation techniques from data collected over time. These curves are key statistical tools for describing populations, but often, their estimators are inefficient when the data are massive, growing and change over time, or too restrictive when the data exhibit measurement errors and a fraction of them are equal to zero. The project expects to develop novel, less restrictive and more realistic nonparametric curve estimation methods in these complex settings. Outcomes include new practical statistical methods and software to benefit experts in diverse fields from nutrition and epidemiology, to environmental science and digital platforms, amongst others.Read moreRead less