Non-parametric estimation of forecast distributions in non-Gaussian state space models

做出准确的预测可以说是经济、商业和金融领域最具挑战性的任务之一，这些领域的数据通常假设为严格的正整数或二进制值，或者具有许多远离平均值的极端值。这个项目将产生新的、最先进的统计方法，以准确估计这些变量的不同可能的未来值所附带的概率。虽然范围很广，但所提倡的技术将对金融领域产生特别的影响，因为在金融领域，未来风险的概念与极端值出现的概率密不可分，因此也与金融变量的未来概率分布密切相关。

The production of accurate forecasts is arguably one of the most challenging tasks in economics, business and finance, where data often assume strictly positive, integer or binary values, or are characterized by many extreme values far from the average. This project will produce new, state-of-the-art statistical methods for generating accurate estimates of the probabilities attached to different possible future values of such variables. Although far-ranging in scope, the techniques advocated will have particular impact in the financial sphere, where the concept of future risk is inextricably linked to the probability of occurrence of extreme values and, hence, to the future probability distribution of the financial variable.