{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260101297"},"data":{"type":"grant-details","id":"DE260101297","attributes":{"code":"DE260101297","administering-organisation":"The University of New South Wales","announcement-administering-organisation":"The University of New South Wales","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-06-01","anticipated-end-date":"2029-05-31","grant-summary":"Advanced statistical methods for complex longitudinal data  . This project aims to develop novel methods for reliable and accurate statistical inference and prediction in complex longitudinal data. The project expects to make significant advances by addressing key challenges in real-world longitudinal analysis, including complex interactions between multiple factors, diverse response types, and unbalanced data—common issues that undermine the reliability of existing methods. This will provide important benefits by improving statistical inference, enabling more reliable predictions for ecology and economic decision-making. These advances will support policymakers and researchers in making informed, data-driven decisions where precision is critical.","funding-current":484523.00,"funding-at-announcement":480678,"investigators-current":[{"title":"Dr","firstName":"Ziyang","familyName":"Lyu","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0003-3307-4148 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Ziyang","familyName":"Lyu","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0003-3307-4148 "}],"organisations-current":[{"organisationName":"The University of New South Wales","roleName":"Administering Organisation","state":"NSW"}],"organisations-at-announcement":[{"organisationName":"The University of New South Wales","roleName":"Administering Organisation","state":"NSW"}],"field-of-research":[{"isPrimary":true,"code":"4905","name":"Statistics","type":"FOR20"},{"isPrimary":false,"code":"490501","name":"Applied Statistics","type":"FOR20"},{"isPrimary":false,"code":"490509","name":"Statistical Theory","type":"FOR20"}],"socio-economic-objective":[{"code":"159902","name":"Ecological Economics","type":"SEO20"},{"code":"159999","name":"Other Economic Framework Not Elsewhere Classified","type":"SEO20"},{"code":"280118","name":"Expanding Knowledge In the Mathematical Sciences","type":"SEO20"}],"international-collaboration":["United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"The Australian Government relies on longitudinal studies to inform key policy decisions in economic planning and ecological conservation.  However, existing statistical methods struggle to provide reliable inference for high-dimensional, unbalanced, and complex longitudinal data, increasing the risk of biased conclusions and suboptimal policies.  This project will develop scalable statistical techniques to improve inference and prediction, ensuring more robust and accurate decision-making. In economics, agencies such as the Australian Treasury and Productivity Commission depend on accurate modeling of workforce trends and income distribution.  Improved statistical methods will enhance economic forecasting and social policy design, reducing risks of misallocated resources.  In ecology, conservation programs such as the National Koala Monitoring Program require precise population estimates to prevent species decline.  The improve statistical methods will enhance biodiversity monitoring, ensuring conservation efforts are timely and effective. The project’s findings will be communicated through academic publications, conference presentations, and collaborations with government agencies.  By providing statistical methodologies tailored to real-world policy challenges, this research will strengthen Australia’s capacity for evidence-based decision-making in economics and environmental management."}}}