权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Latent Variable and Long-Memory Models

潜变量和长记忆模型

基本信息

批准号：
1357401
负责人：
Susanne Schennach
金额：
$ 19.88万
依托单位：
Brown University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-07-01 至 2017-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1357401&HistoricalAwards=false
关键词：
Latent Variable Long Memory Models

项目摘要

The proposed work is divided into two projects. The first project develops computationally efficient statistical inference techniques for classes of set identified models. Such models are helpful because they seek, under minimal assumptions, to usefully constrain the possible values of a model's parameters while avoiding the unnecessarily strong assumptions that would be needed to isolate a unique solution. Specifically, this project considers set identified models that arise from the presence of unobservable variables in the model and that have characteristics that traditionally demand a high-dimensional treatment. The methods proposed aim to express asymptotic properties in terms of a combination of simpler low-dimensional building blocks and may thus offer considerable advantages over existing generic brute-force simulation methods. The second project explores a connection between two apparently disparate concepts: (i) long memory (i.e. shocks have persistent effects on a dynamical system) and (ii) network structure. This project demonstrates that long memory can naturally arise when a large number of subsystems with a short memory are interconnected to form a network such that the outputs of each of the subsystems are fed into the inputs of others. This results in a collective behavior that is richer than that of individual subsystems. The long-memory behavior is found to be primarily determined by the geometry of the network rather than by the specific dynamic response of individual subsystems. These finding are interesting because, although long-memory processes are routinely used in time series modeling, a simple constructive explanation for their occurrence had so far remained difficult to find.Set identified models are becoming widely used in statistics and economics, and this trend will likely continue, especially if inference methods can be made simpler and computationally more efficient. Fields as diverse as medicine and climate change could also benefit from formal methods acknowledging that some parameters cannot be precisely known but can plausibly be bounded. Understanding how network structure influences the dynamics of an economy is a central question, especially in the context of the recent credit crisis. The collective behavior of networks has clear applications in social sciences in general, including psychology, the study of social media, and even computer networks, which are being increasingly relied upon for infrastructure management and logistics.

拟开展的工作分为两个项目。第一个项目为集合识别模型的类别开发计算高效的统计推断技术。这种模型是有帮助的，因为它们寻求在最小的假设下，有效地约束模型参数的可能值，同时避免分离唯一解所需的不必要的强烈假设。具体地说，这个项目考虑了集合识别的模型，这些模型是由于模型中存在不可观察到的变量而产生的，并且具有传统上需要高维处理的特征。所提出的方法旨在用更简单的低维积木的组合来表示渐近性质，因此可能比现有的一般蛮力模拟方法具有相当大的优势。第二个项目探讨了两个明显不同的概念之间的联系：(1)长记忆(即电击对动力系统具有持续性影响)和(2)网络结构。这个项目证明，当大量具有短记忆的子系统相互连接以形成网络，使得每个子系统的输出被馈送到其他子系统的输入时，自然会产生长记忆。这导致了比单个子系统更丰富的集体行为。研究发现，长记忆行为主要由网络的几何结构决定，而不是由单个子系统的特定动态响应决定。这些发现很有趣，因为尽管长记忆过程经常被用于时间序列建模，但到目前为止，对于它们的出现仍然很难找到简单的建设性解释。集合识别的模型正越来越广泛地应用于统计学和经济学，这一趋势可能会继续下去，特别是如果推理方法可以变得更简单和计算更有效的话。从医学和气候变化等不同的领域也可以受益于正式的方法，承认一些参数不能准确知道，但看起来可能是有界的。了解网络结构如何影响一个经济体的动态是一个中心问题，尤其是在最近的信贷危机背景下。网络的集体行为在整个社会科学中都有明显的应用，包括心理学、社交媒体研究，甚至是越来越多地依赖于基础设施管理和后勤的计算机网络。