学术报告
Adaptively aggregated forecast for exponential family panel model
作者：
时间： 2024-06-14
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题目：Adaptively aggregated forecast for exponential family panel model
报告人：喻达磊 (西安交通大学)
摘要：Aggregation strategies, playing an important role akin to that of model selection, have been extensively studied in different
statistical models to improve forecasting accuracy. However, traditional aggregated forecast strategies for panel data are mainly
developed under the assumption that response variables are continuously distributed (or normally distributed). Replacing this
assumption by a more general family of distributions, i.e., exponential family distributions, this paper proposes a computationally
efficient way to construct the cumulative risk function and to explicitly accommodate correlation structure of within-subject
observations, develops two novel adaptively aggregated forecasting strategies via exponential reweighting and quadratic
reweighting, and rigorously establishes the corresponding tight oracle inequalities. The proposed exponential reweighting based
strategy enjoys promising Kullback-Leibler risk bound adaptation. Moreover, under the quadratic risk, a promising adaptation
property can be achieved by the quadratic reweighting based strategy. The risk bound properties of the two proposed procedures
in the presence of pre-screening are established under mild conditions. The calibration properties of the proposed methods are also
analyzed. Simulation studies, together with an example in analyzing television viewers' binary decision sequence of watching drama
episodes, verify the superiority of our methods over existing model selection and aggregation methods.
报告人简介：
喻达磊，博士（香港城市大学），西安交通大学数学与统计学院教授，博士生导师。研究领域为模型选
择、模型平均、估计理论和统计极限理论等，一些成果发表在 JRSS-B、JASA、JBES 和中国科学：数学上。入
选了国家高层次青年人才计划和西安交通大学校内青拔 A 类支持计划。
报告时间：6 月 14 日（周五）上午 10:30-11:30
报告地点：教二楼 913 教室
联系人：邹国华
