主講人：張新雨 中國(guó)科學(xué)院數(shù)學(xué)與系統(tǒng)科學(xué)研究院研究員
時(shí)間：2025年4月15日14：00
地點(diǎn)：三號(hào)樓332室
舉辦單位：數(shù)理學(xué)院
主講人介紹：張新雨，中科院數(shù)學(xué)與系統(tǒng)科學(xué)研究院/預(yù)測(cè)中心研究員。主要從事計(jì)量經(jīng)濟(jì)學(xué)和統(tǒng)計(jì)學(xué)的理論和應(yīng)用研究工作，具體研究方向包括模型平均、機(jī)器學(xué)習(xí)、組合預(yù)測(cè)和醫(yī)學(xué)統(tǒng)計(jì)等。2010年在中科院系統(tǒng)所獲博士學(xué)位，曾是TAMU博士后和PSU的Research Fellow。擔(dān)任期刊《JSSC》領(lǐng)域主編、期刊《SADM》、《系統(tǒng)科學(xué)與數(shù)學(xué)》、《應(yīng)用概率統(tǒng)計(jì)》等的AE或編委，是雙法學(xué)會(huì)數(shù)據(jù)科學(xué)分會(huì)副理事長(zhǎng)、國(guó)際統(tǒng)計(jì)學(xué)會(huì)當(dāng)選會(huì)員和智源青年科學(xué)家。先后主持國(guó)家自然科學(xué)基金委優(yōu)秀和杰出青年研究基金項(xiàng)目，曾獲得中國(guó)管理學(xué)青年獎(jiǎng)和中科院優(yōu)秀博士學(xué)位論文等獎(jiǎng)勵(lì)。發(fā)表了50多篇學(xué)術(shù)論文,其中20余篇論文發(fā)表在A(yíng)nnals of Statistics、Biometrika、JASA、JRSSB、Journal of Econometrics和Econometric Theory。
內(nèi)容介紹：In recent years, model averaging, by which estimates are obtained based on not one single model but a weighted ensemble of models, has received growing attention as an alternative to model selection. To-date, methods for model averaging have been developed almost exclusively for point-valued data, despite the fact that interval-valued data are commonplace in many applications and the substantial body of literature on estimation and inference methods for interval-valued data. This paper focuses on the special case of interval time series data, and assumes that the mid-point and log-range of the interval values are modelled by a two-equation vector autoregressive with exogenous covariates (VARX) model. We develop a methodology for combining models of varying lag orders based on a weight choice criterion that minimises an unbiased estimator of the squared error risk of the model average estimator. We prove that this method yields predictors of mid-points and ranges with an optimal asymptotic property. In addition, we develop a method for correcting the range forecasts, taking into account the forecast error variance. An extensive simulation experiment examines the performance of the proposed model averaging method in finite samples. We apply the method to an interval-valued data series on crude oil future prices.