報(bào)告題目：Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition
主講人：練恒副教授（香港城市大學(xué)）
時(shí)間：2019年11月28日（周四）10:00 a.m.
地點(diǎn)：北院卓遠(yuǎn)樓305會(huì)議室
主辦單位：統(tǒng)計(jì)與數(shù)學(xué)學(xué)院
摘要：Abstract: The VAR model involves a large number of parameters so it can suffer from the curse of dimensionality for high-dimensional time series data. The reduced-rank coefficient model can alleviate the problem but the low-rank structure along the time direction for time series models has never been considered. We rearrange the parameters in the VAR model to a tensor form, and propose a multilinear low-rank VAR model via tensor decomposition that effectively exploits the temporal and cross-sectional low-rank structure. Effectiveness of the methods is demonstrated on simulated and real data.
主講人簡介：
練恒，現(xiàn)任香港城市大學(xué)數(shù)學(xué)系副教授，于2000年在中國科學(xué)技術(shù)大學(xué)獲得數(shù)學(xué)和計(jì)算機(jī)學(xué)士學(xué)位，2007年在美國布朗大學(xué)獲得計(jì)算機(jī)碩士，經(jīng)濟(jì)學(xué)碩士和應(yīng)用數(shù)學(xué)博士學(xué)位。研究方向包括高維數(shù)據(jù)分析，函數(shù)數(shù)據(jù)分析，機(jī)器學(xué)習(xí)等。在《Journal of the Royal Statistical Society，Series B》、《Journal of the American Statistical Association》等國際頂級統(tǒng)計(jì)學(xué)期刊上發(fā)表高水平學(xué)術(shù)論文30多篇。
滇公網(wǎng)安備53010202000350號滇ICP備17008047號 Copyright ? 2013 Yunnan University of Finance and Economics All Rights Reserved
