报告人：江艺烨（法国Université Grenoble Aples）

时 间：2024年2月28日15:30

地 点：海韵园数理大楼686会议室

内容摘要:

In this work, we propose a new autoregressive model for multivariate distributional time series. We consider a collection of N series of probability measures supported over a bounded interval in R, which are indexed by distinct time instants. Especially, we wish to develop such a model which can identify the dependency structure in the temporal evolution of the measures. To this end, we adopt the Wasserstein metric. We establish the regression model in the Tangent space of the Lebesgue measure by first "centering" all the raw measures so that their Fréchet means turn Lebesgue. The uniqueness and stationarity results are provided. We also propose a consistent estimator for the model coefficient. In addition to the simulated data, the proposed model is illustrated on a real data set of age distributions of countries.

个人简介：

江艺烨，法国格勒诺布尔阿尔卑斯大学博士后，现主要从事时空数据统计建模分析与应用的研究。2022年博士毕业于法国波尔多大学。2015-2018年为伟德国际1946源自英国概率论与数理统计系硕士，其中2017-2018年通过厦大数院双硕士项目赴法学习。

联系人：胡杰