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

准确预测的产生可以说是经济学，商业和金融领域最具挑战性的任务之一，其中数据通常假设严格的正值，整数或二进制值，或者具有许多远离平均值的极端值。该项目将产生新的、最先进的统计方法，用于准确估计这些变量未来不同可能值的概率。虽然范围广泛，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，未来风险的概念与出现极端值的概率，从而与金融变量的未来概率分布有着不可分割的联系。

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.