應(yīng)yl7703永利官網(wǎng)李周平教授邀請(qǐng)，南京審計(jì)大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)學(xué)院孔新兵教授將于2021年7月2日至7月3日訪問(wèn)我校，期間將舉辦專題學(xué)術(shù)報(bào)告。

報(bào)告題目：Learning Quantile Factors for Large-dimensional Time Series with Statistical Guarantee

報(bào)告時(shí)間：7月2日（星期五）下午4：00

報(bào)告地點(diǎn)：理工樓631報(bào)告廳

摘 要：Quantile is an important measure in risk control in finance and quality assessment in service industry. In this paper, we model the temporal and cross-sectional interactive effect of the quantiles of a large-dimensional time series by a latent quantile factor model. The factor loadings and scores are learnt with statistical guarantee via an iterative check-loss-minimization procedure. Without any moment constraint on the idiosyncratic errors, we correctly identify the common and idiosyncratic components for each variable. We obtained the statistical convergence rates of the minimization estimators. Bahardur representations for the estimated factor loadings and scores are provided under some mild conditions. Moreover, a robust method is proposed to select the number of factors consistently. Simulation experiments checked the validity of the theory. Our analysis on a financial data set shows the superiority of the proposed method over other state-of-the-art methods.

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孔新兵教授簡(jiǎn)介

孔新兵，南京審計(jì)大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)學(xué)院教授、院長(zhǎng)；國(guó)際統(tǒng)計(jì)協(xié)會(huì)(ISI)當(dāng)選會(huì)員；獲2012年度香港數(shù)學(xué)會(huì)“最佳博士論文獎(jiǎng)”；主要研究興趣為高頻數(shù)據(jù)分析、髙維因子分析和經(jīng)濟(jì)金融計(jì)量分析；擔(dān)任Random Matrices-Theory and Application和《應(yīng)用概率統(tǒng)計(jì)》編委；中國(guó)現(xiàn)場(chǎng)統(tǒng)計(jì)研究會(huì)多個(gè)分會(huì)常務(wù)理事；在統(tǒng)計(jì)學(xué)頂級(jí)期刊JASA、AoS、Biometrika、JoE發(fā)表論文16篇，獨(dú)立發(fā)表AoS、Biometrika 3篇；主持國(guó)家基金項(xiàng)目4項(xiàng)。入選國(guó)家高層次青年人才計(jì)劃。

甘肅應(yīng)用數(shù)學(xué)中心

蘭州大學(xué)大數(shù)據(jù)科學(xué)研究中心

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