学而不已 | 经济与管理学部一周学术讲座概览（11月15日-11月21日）

华东师范大学经济与管理学部专业学位教育中心
2021-11-14 12:58 浏览量: 3244
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讲座总览 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...

讲座总览

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.陈家骅：Density ratio model with data-adaptive basis function2.赵普映：Bayesian Empirical Likelihood Inference With Complex Survey Data3.王磊：Estimation and inference for multi-kink expectile regression with longitudinal data

详细讲座信息

1

时间：2021 年11月15 日（周一）10：00-11：30地点：线上，腾讯ID: 662 023 056题目：The Sunshine Effects on Solar Loan Repayments主讲人：秦雨新加坡国立大学商学院房地产系副教授主持人：李莉 助理教授主办：宏观经济学团队摘要：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.报告人简介：秦雨现任新加坡国立大学商学院房地产系长聘副教授，2014年在康奈尔大学获应用经济学博士学位。担任 China Economic Review的共同主编和Journal Economic Geography的编委会委员。研究领域主要包括交通经济学、环境经济学、住房和土地市场等。其学术成果发表在Nature Climate Change、Journal of Public Economics、Journal of Environmental Economics and Management等学术期刊。

2

时间：2021年11月17日（周三）10：00-11：00地点：线上，腾讯会议：680 707 248题目：Functional central limit theorems for stick-breaking priors主讲人：Yaozhong Hu加拿大阿尔伯塔大学数学和统计科学系教授主持人：徐方军 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.报告人简介：Yaozhong Hu(胡耀忠), 加拿大阿尔伯塔大学Centennial教授，1981年获江西大学计算数学学士学位，1984年获中科院武汉数学物理研究所硕士学位，1992年获法国路易斯巴斯德大学概率博士学位，师从国际著名概率学家P. A. Meyer教授。胡教授的研究兴趣广泛，主要研究领域是随机分析、数理金融、随机控制、随机微分方程数值分析等。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等概率论和数学综合类top期刊上发表论文100多篇，出版专著2部。2015年，当选为Fellow of Institute of Mathematical Statistics。

3

时间：2021年11月18日（周四） 10：00-11：30地点：线上，腾讯会议：685 263 364题目：Density ratio model with data-adaptive basis function主讲人：陈家骅云南大学&英属哥伦比亚大学教授主持人：刘玉坤 教授主办：统计与数据科学前沿理论及应用教育部重点实验室摘要：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.报告人简介：陈家骅，加拿大英属哥伦比亚大学（UBC）统计系国家一级讲座教授，云南大学大数据研究院院长。曾任泛华统计学会主席、加拿大统计杂志主编等职务。1983年本科毕业于中国科大数学系，1985年硕士毕业于中国科学院系统科学研究所，1990年于美国威斯康星大学麦迪逊分校统计学系获得博士学位，师从吴建福教授。研究兴趣包括混合模型、试验设计、经验似然、大样本理论和变量选择等多个统计研究领域，在顶级统计学期刊如JASA, JRSSB, Annals of Statistics, Biometrika等上发表论文100多篇。曾获多项学术荣誉：2005年被加拿大统计学会授予CRM-SSC年度奖；2005年当选fellow of the Institute of Mathematical Statistics；2009年当选fellow of the America Statistical Associate；2014年获加拿大统计学会最高金奖；2016年获泛华统计协会杰出成就奖。

时间：2021年11月18日（周四）13：00-13：50地点：线上，腾讯会议305 493 290题目：Bayesian Empirical Likelihood Inference With Complex Survey Data主讲人：赵普映云南大学副教授主持人：唐炎林 研究员摘要：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.报告人简介：赵普映，博士，云南大学数学与统计学院副教授、博士生导师，现主持国家自然科学基金面上项目1项。

时间：2021年11月18日（周四）13：50-14：40地点：线上，腾讯会议305 493 290题目：Estimation and inference for multi-kink expectile regression withlongitudinal data主讲人：王磊南开大学副研究员主持人：唐炎林 研究员摘要：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..报告人简介：王磊，南开大学统计与数据科学学院副研究员，博导，南开大学百名青年学科带头人。研究方向是统计学习和复杂数据分析，已在Biometrika、Bernoulli、Statistica Sinica、Scandinavian Journal of Statistics等统计学杂志发表学术论文30多篇，主持国家自然科学基金青年、面上项目及天津市自然科学基金各一项。现任中国现场统计研究会生存分析分会副秘书长，Journal of Nonparametics Statistics的Associate Editor，泛华统计协会永久会员, 荣获上海市优秀博士学位论文等。

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