Mitigating bias in statistical analyses of data collected over time

减少随时间收集的数据统计分析中的偏差

• 批准号: DP230100147
• 负责人: Prof Aurore Delaigle
• 金额: $ 29.96万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-03-22 至 2026-03-21
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP230100147
• 关键词: Mitigating; bias; statistical; analyses; data

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.

该项目旨在开发创新的非参数分布和回归曲线估计技术，从随着时间的推移收集的数据。这些曲线是描述总体的关键统计工具，但通常，当数据庞大，随着时间的推移而增长和变化时，它们的估计量效率低下，或者当数据显示测量误差并且其中一小部分等于零时，它们的估计量限制太多。该项目希望在这些复杂的环境中开发新的、限制较少的和更现实的非参数曲线估计方法。成果包括新的实用统计方法和软件，使营养和流行病学、环境科学和数字平台等不同领域的专家受益。