Mitigating bias in statistical analyses of data collected over time

该项目旨在根据一段时间收集的数据开发创新的非参数分布和回归曲线估计技术。这些曲线是描述总体的关键统计工具，但通常，当数据庞大、增长和随时间变化时，它们的估计器效率低下，或者当数据显示测量误差时，或者当数据的一小部分等于零时，它们的估计器过于严格。该项目希望在这些复杂的环境中开发出新的、限制较少的、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，使从营养和流行病学到环境科学和数字平台等不同领域的专家受益。

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.