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學而不已 | 經(jīng)濟與管理學部一周學術講座概覽（11月15日-11月21日）

華東師范大學經(jīng)濟與管理學部專業(yè)學位教育中心
2021-11-14 12:58 瀏覽量: 3219
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講座總覽 一、2021年11月15日（周一）1.秦雨：The Sunshine Effects on Solar Loan Repayments二、2021年11月17日（周三）1. Yaozhong...

講座總覽

一、2021年11月15日（周一）1.秦雨：The Sunshine Effects on Solar Loan Repayments二、2021年11月17日（周三）1. Yaozhong Hu：Functional central limit theorems for stick-breaking priors三、2021年11月18日（周四）1.陳家驊：Density ratio model with data-adaptive basis function2.趙普映：Bayesian Empirical Likelihood Inference With Complex Survey Data3.王磊：Estimation and inference for multi-kink expectile regression with longitudinal data

詳細講座信息

1

時間：2021 年11月15 日（周一）10：00-11：30地點：線上，騰訊ID: 662 023 056題目：The Sunshine Effects on Solar Loan Repayments主講人：秦雨新加坡國立大學商學院房地產(chǎn)系副教授主持人：李莉 助理教授主辦：宏觀經(jīng)濟學團隊摘要：Solar loans are increasingly used to promote residential solar photovoltaic expansion. However, a critical problem with financing residential solar energy is the high default rate. This paper studies the psychological effects of sunshine on borrowers’ repayment behaviors. Using administrative datasets from China, we show that borrowers are 20.8 percent less likely to be delinquent if the sunshine duration is one standard deviation longer in the week of repayment deadline. The evidence is most consistent with behavioral bias that borrowers mispredict future revenue based on the current weather conditions. Other explanations such as intertemporal substitution, liquidity constraints, strategic default, or moods are less consistent with the evidence. Furthermore, borrowers partially learn from past experiences. We highlight the importance of psychological factors in loan design, particularly in the renewable energy sector.報告人簡介：秦雨現(xiàn)任新加坡國立大學商學院房地產(chǎn)系長聘副教授，2014年在康奈爾大學獲應用經(jīng)濟學博士學位。擔任 China Economic Review的共同主編和Journal Economic Geography的編委會委員。研究領域主要包括交通經(jīng)濟學、環(huán)境經(jīng)濟學、住房和土地市場等。其學術成果發(fā)表在Nature Climate Change、Journal of Public Economics、Journal of Environmental Economics and Management等學術期刊。

2

時間：2021年11月17日（周三）10：00-11：00地點：線上，騰訊會議：680 707 248題目：Functional central limit theorems for stick-breaking priors主講人：Yaozhong Hu加拿大阿爾伯塔大學數(shù)學和統(tǒng)計科學系教授主持人：徐方軍 教授摘要：I will talk aboutthe strong law of large numbers,Glivenko-Cantelli theorem, central limit theorem,functional central limit theorem for various nonparametric Bayesian priors which include the stick-breaking process with general stick-breaking weights, the two-parameter Poisson-Dirichlet process, the normalized inverse Gaussian process, the normalized generalized gamma process, and the generalized Dirichlet process. For the stick-breaking process with general stick-breaking weights, we will explain two general conditions such that the central limit theorem and functional central limit theorem hold. Except in the case of the generalized Dirichlet process, since the finite dimensional distributions of these processes are either hard to obtain or arecomplicated to use even they are available,weuse themethod of momentsto obtain the convergence results.For the generalized Dirichlet process we use its marginal distributions to obtain the asymptotics although the computations are highly technical. This is joint work with Junxi Zhang.報告人簡介：Yaozhong Hu(胡耀忠), 加拿大阿爾伯塔大學Centennial教授，1981年獲江西大學計算數(shù)學學士學位，1984年獲中科院武漢數(shù)學物理研究所碩士學位，1992年獲法國路易斯巴斯德大學概率博士學位，師從國際著名概率學家P. A. Meyer教授。胡教授的研究興趣廣泛，主要研究領域是隨機分析、數(shù)理金融、隨機控制、隨機微分方程數(shù)值分析等。在 Ann. Probability、Probab. Theory Related Fields、Ann. Applied Probability、Bernoulli、Stochatis Process. Appl.、Mem. Amer. Math. Soc.、Comm. PDEs、J. Funct. Anal、Trans. Amer. Math. Soc等概率論和數(shù)學綜合類top期刊上發(fā)表論文100多篇，出版專著2部。2015年，當選為Fellow of Institute of Mathematical Statistics。

3

時間：2021年11月18日（周四） 10：00-11：30地點：線上，騰訊會議：685 263 364題目：Density ratio model with data-adaptive basis function主講人：陳家驊云南大學&英屬哥倫比亞大學教授主持人：劉玉坤 教授主辦：統(tǒng)計與數(shù)據(jù)科學前沿理論及應用教育部重點實驗室摘要：In many applications, we collect samples from interconnected populations. These population distributions share some latent structure, so it is advantageous to jointly analyze the samples to enhance statistical efficiency. One effective way to connect the distributions is the density ratio model (DRM). A key ingredient in the DRM is that the log density ratios are linear combinations of pre-specified functions; the vector formed by these functions is called the basis function. The benefit of DRM, however, relies on correctly specifying the basis function. In applications, we do not have complete knowledge to enable a perfect choice of the basis function. A data-adaptive choice of the basis function can alleviate the risk of model misspecification, and it remains an open problem. In this talk, we discuss a data-adaptive approach to the choice of basis function based on functional principal component analysis (FPCA). Under some conditions, we show that this approach leads to consistent basis function estimation. Our simulation results show that the proposed adaptive choice leads to an efficiency gain. We use a house income data set to demonstrate the efficiency gain and the ease of our approach.報告人簡介：陳家驊，加拿大英屬哥倫比亞大學（UBC）統(tǒng)計系國家一級講座教授，云南大學大數(shù)據(jù)研究院院長。曾任泛華統(tǒng)計學會主席、加拿大統(tǒng)計雜志主編等職務。1983年本科畢業(yè)于中國科大數(shù)學系，1985年碩士畢業(yè)于中國科學院系統(tǒng)科學研究所，1990年于美國威斯康星大學麥迪遜分校統(tǒng)計學系獲得博士學位，師從吳建福教授。研究興趣包括混合模型、試驗設計、經(jīng)驗似然、大樣本理論和變量選擇等多個統(tǒng)計研究領域，在頂級統(tǒng)計學期刊如JASA, JRSSB, Annals of Statistics, Biometrika等上發(fā)表論文100多篇。曾獲多項學術榮譽：2005年被加拿大統(tǒng)計學會授予CRM-SSC年度獎；2005年當選fellow of the Institute of Mathematical Statistics；2009年當選fellow of the America Statistical Associate；2014年獲加拿大統(tǒng)計學會最高金獎；2016年獲泛華統(tǒng)計協(xié)會杰出成就獎。

時間：2021年11月18日（周四）13：00-13：50地點：線上，騰訊會議305 493 290題目：Bayesian Empirical Likelihood Inference With Complex Survey Data主講人：趙普映云南大學副教授主持人：唐炎林 研究員摘要：We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and nondifferentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically valid design-based frequentist properties under single-stage unequal probability sampling designs with small sampling fractions. Large sample properties of the Bayesian inference proposed are established for both non-informative and informative priors under the design-based framework. We also propose a Bayesian model selection procedure with complex survey data and show that it works for general sampling designs. An efficient Markov chain Monte Carlo procedure is described for the required computation of the posterior distribution for general vector parameters. Simulation studies and an application to a real survey data set are included to examine the finite sample performances of the methods proposed as well as the effect of different types of prior and different types of sampling design. This is a joint work withMalay Ghosh, J.N.K. Rao and Changbao Wu.報告人簡介：趙普映，博士，云南大學數(shù)學與統(tǒng)計學院副教授、博士生導師，現(xiàn)主持國家自然科學基金面上項目1項。

時間：2021年11月18日（周四）13：50-14：40地點：線上，騰訊會議305 493 290題目：Estimation and inference for multi-kink expectile regression withlongitudinal data主講人：王磊南開大學副研究員主持人：唐炎林 研究員摘要：In this paper, we investigate parameter estimation, kink points testing and statistical inference for a longitudinal multi-kink expectile regression model. The estimators for the kink locations and regression coefficients are obtained by using a bootstrap restarting iterative algorithm to avoid local minima. A backward selection procedure based on a modified BIC is applied to estimate the number of kink points. We theoretically demonstrate the number selection consistency of kink points and the asymptotic normality of all estimators. In particular, the estimators of kink locations are shown to achieve root-n consistency. A weighted cumulative sum type statistic is proposed to test the existence of kink effects at a given expectile, and its limiting distributions are derived under both the null and the local alternative hypotheses. The traditional Wald-type and cluster bootstrap confidence intervals for kink locations are also constructed. Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic errors. Two applications to the Nation Growth, Lung and Health Study and Capital Bike sharing dataset in Washington D.C. are also presented..報告人簡介：王磊，南開大學統(tǒng)計與數(shù)據(jù)科學學院副研究員，博導，南開大學百名青年學科帶頭人。研究方向是統(tǒng)計學習和復雜數(shù)據(jù)分析，已在Biometrika、Bernoulli、Statistica Sinica、Scandinavian Journal of Statistics等統(tǒng)計學雜志發(fā)表學術論文30多篇，主持國家自然科學基金青年、面上項目及天津市自然科學基金各一項?，F(xiàn)任中國現(xiàn)場統(tǒng)計研究會生存分析分會副秘書長，Journal of Nonparametics Statistics的Associate Editor，泛華統(tǒng)計協(xié)會永久會員, 榮獲上海市優(yōu)秀博士學位論文等。

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編輯|蘭雨涵
編輯：劉蕊

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