Generalised Degrees of Freedom and Probabilistic Regularisation

该项目旨在开发新的统计工具，通过考虑模型的复杂性和与拟合过程相关的不确定性，进行更准确的预测。该项目还计划通过新的惩罚函数开发一种新的收缩方法，以避免过度拟合和渐近性质。关键的应用可能包括预测因子数量很大的遗传研究和经常收集多变量和时间数据的生物实验-例如动物和鱼类养殖中的经济育种以及在人类，动物和植物的遗传研究中更有效地检测感兴趣的基因。

This project intends to develop novel statistical tools for more accurate prediction by taking account of model complexity and uncertainties associated with the fitting procedure. The project also plans to develop a novel shrinkage approach via new penalty functions to avoid over-fitting and asymptotic properties. The key applications may include genetic studies where the number of predictors is large and biological experiments where multivariate and temporal data are often collected – for example economical breeding in animal and fish farming and more effectively detecting the genes of interest in genetic studies on human, animals and plants.