Prediction, inference and their application to modelling correlated data

该项目旨在通过发明复杂和新颖的重采样方案（如广义快速自举和重复部分置换），为各种相关数据类型的预测和推断创造新的改进方法。这项研究将对统计学的理论和实践以及使用混合或组成模型来分析相关数据的实质性领域产生影响。这将大大改善社会、生态和地质科学等领域统计模型的评估和稳定性。

This project aims to create new, improved methods for prediction and making inference about predictions for a variety of correlated data types through inventing sophisticated and novel resampling schemes such as the generalised fast bootstrap and repeated partial permutation. The research will impact on both the theory and practice of statistics and on substantive fields which use mixed or compositional models to analyse dependent data. This will be a significant improvement in the assessment and stability of statistical models in areas such as social, ecological and geological sciences.