Problems of identification and inference for 'non-standard' models in complex systems with special reference to finance and teletraffic

复杂系统中“非标准”模型的识别和推理问题，特别涉及金融和电信业务。该项目关注的是处理复杂系统所需的“非标准”模型，例如那些表现出缩放和分形特性的模型。重点是处理重尾分布和长距离相关观测的方法，大多数标准统计方法都会分解，以及在金融和电信中的应用。该项目的一个重要组成部分涉及模型验证海德的分形活动时间几何布朗运动模型，一个候选人的最小描述风险资产模型，以取代几何布朗运动范式。

Problems of identification and inference for 'non-standard' models in complex systems with special reference to finance and teletraffic. The project is concerned with 'non-standard' models needed to deal with complex systems, such as those exhibiting scaling and fractal properties. There is a focus on methods for dealing with heavy tailed distributions and long range dependent observations, for which most standard statistical methods break down, and on applications in finance and telecommunications. An important part of the project concerns model validation for Heyde's fractal activity time geometric Brownian motion model, a candidate minimal description risky asset model to replace the geometric Brownian motion paradigm.