蛋播视频一区,无码鲁丝一区二区,精品 久久 五月天,国产老熟女,五月草草在线观看,中文日韩欧美,情色一区二区三区,欧美日韩亚洲激情在线,亚洲制服在线香蕉

6月1日直播預(yù)告：香港理工大學(xué)SPEED學(xué)院_全新碩士課程專(zhuān)場(chǎng)！26fall入學(xué)！

學(xué)而不已 | 經(jīng)濟(jì)與管理學(xué)部一周學(xué)術(shù)講座概覽（11月15日-11月21日）

華東師范大學(xué)經(jīng)濟(jì)與管理學(xué)部專(zhuān)業(yè)學(xué)位教育中心
2021-11-14 12:58 瀏覽量: 3631
?智能總結(jié)

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(xì)講座信息

1

時(shí)間：2021 年11月15 日（周一）10：00-11：30地點(diǎn)：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(guó)立大學(xué)商學(xué)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(jì)學(xué)團(tuán)隊(duì)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(bào)告人簡(jiǎn)介：秦雨現(xiàn)任新加坡國(guó)立大學(xué)商學(xué)院房地產(chǎn)系長(zhǎng)聘副教授，2014年在康奈爾大學(xué)獲應(yīng)用經(jīng)濟(jì)學(xué)博士學(xué)位。擔(dān)任 China Economic Review的共同主編和Journal Economic Geography的編委會(huì)委員。研究領(lǐng)域主要包括交通經(jīng)濟(jì)學(xué)、環(huán)境經(jīng)濟(jì)學(xué)、住房和土地市場(chǎng)等。其學(xué)術(shù)成果發(fā)表在Nature Climate Change、Journal of Public Economics、Journal of Environmental Economics and Management等學(xué)術(shù)期刊。

2

時(shí)間：2021年11月17日（周三）10：00-11：00地點(diǎn)：線上，騰訊會(huì)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(xué)數(shù)學(xué)和統(tǒng)計(jì)科學(xué)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(bào)告人簡(jiǎn)介：Yaozhong Hu(胡耀忠), 加拿大阿爾伯塔大學(xué)Centennial教授，1981年獲江西大學(xué)計(jì)算數(shù)學(xué)學(xué)士學(xué)位，1984年獲中科院武漢數(shù)學(xué)物理研究所碩士學(xué)位，1992年獲法國(guó)路易斯巴斯德大學(xué)概率博士學(xué)位，師從國(guó)際著名概率學(xué)家P. A. Meyer教授。胡教授的研究興趣廣泛，主要研究領(lǐng)域是隨機(jī)分析、數(shù)理金融、隨機(jī)控制、隨機(jī)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(xué)綜合類(lèi)top期刊上發(fā)表論文100多篇，出版專(zhuān)著2部。2015年，當(dāng)選為Fellow of Institute of Mathematical Statistics。

3

時(shí)間：2021年11月18日（周四） 10：00-11：30地點(diǎn)：線上，騰訊會(huì)議：685 263 364題目：Density ratio model with data-adaptive basis function主講人：陳家驊云南大學(xué)&英屬哥倫比亞大學(xué)教授主持人：劉玉坤 教授主辦：統(tǒng)計(jì)與數(shù)據(jù)科學(xué)前沿理論及應(yīng)用教育部重點(diǎn)實(shí)驗(yàn)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(bào)告人簡(jiǎn)介：陳家驊，加拿大英屬哥倫比亞大學(xué)（UBC）統(tǒng)計(jì)系國(guó)家一級(jí)講座教授，云南大學(xué)大數(shù)據(jù)研究院院長(zhǎng)。曾任泛華統(tǒng)計(jì)學(xué)會(huì)主席、加拿大統(tǒng)計(jì)雜志主編等職務(wù)。1983年本科畢業(yè)于中國(guó)科大數(shù)學(xué)系，1985年碩士畢業(yè)于中國(guó)科學(xué)院系統(tǒng)科學(xué)研究所，1990年于美國(guó)威斯康星大學(xué)麥迪遜分校統(tǒng)計(jì)學(xué)系獲得博士學(xué)位，師從吳建福教授。研究興趣包括混合模型、試驗(yàn)設(shè)計(jì)、經(jīng)驗(yàn)似然、大樣本理論和變量選擇等多個(gè)統(tǒng)計(jì)研究領(lǐng)域，在頂級(jí)統(tǒng)計(jì)學(xué)期刊如JASA, JRSSB, Annals of Statistics, Biometrika等上發(fā)表論文100多篇。曾獲多項(xiàng)學(xué)術(shù)榮譽(yù)：2005年被加拿大統(tǒng)計(jì)學(xué)會(huì)授予CRM-SSC年度獎(jiǎng)；2005年當(dāng)選fellow of the Institute of Mathematical Statistics；2009年當(dāng)選fellow of the America Statistical Associate；2014年獲加拿大統(tǒng)計(jì)學(xué)會(huì)最高金獎(jiǎng)；2016年獲泛華統(tǒng)計(jì)協(xié)會(huì)杰出成就獎(jiǎng)。

時(shí)間：2021年11月18日（周四）13：00-13：50地點(diǎn)：線上，騰訊會(huì)議305 493 290題目：Bayesian Empirical Likelihood Inference With Complex Survey Data主講人：趙普映云南大學(xué)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(bào)告人簡(jiǎn)介：趙普映，博士，云南大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院副教授、博士生導(dǎo)師，現(xiàn)主持國(guó)家自然科學(xué)基金面上項(xiàng)目1項(xiàng)。

時(shí)間：2021年11月18日（周四）13：50-14：40地點(diǎn)：線上，騰訊會(huì)議305 493 290題目：Estimation and inference for multi-kink expectile regression withlongitudinal data主講人：王磊南開(kāi)大學(xué)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(bào)告人簡(jiǎn)介：王磊，南開(kāi)大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)學(xué)院副研究員，博導(dǎo)，南開(kāi)大學(xué)百名青年學(xué)科帶頭人。研究方向是統(tǒng)計(jì)學(xué)習(xí)和復(fù)雜數(shù)據(jù)分析，已在Biometrika、Bernoulli、Statistica Sinica、Scandinavian Journal of Statistics等統(tǒng)計(jì)學(xué)雜志發(fā)表學(xué)術(shù)論文30多篇，主持國(guó)家自然科學(xué)基金青年、面上項(xiàng)目及天津市自然科學(xué)基金各一項(xiàng)?，F(xiàn)任中國(guó)現(xiàn)場(chǎng)統(tǒng)計(jì)研究會(huì)生存分析分會(huì)副秘書(shū)長(zhǎng)，Journal of Nonparametics Statistics的Associate Editor，泛華統(tǒng)計(jì)協(xié)會(huì)永久會(huì)員, 榮獲上海市優(yōu)秀博士學(xué)位論文等。

歡迎參加

編輯|蘭雨涵
內(nèi)容編輯：劉蕊

(本文轉(zhuǎn)載自 ，如有侵權(quán)請(qǐng)電話聯(lián)系13810995524)

* 文章為作者獨(dú)立觀點(diǎn)，不代表MBAChina立場(chǎng)。采編部郵箱：news@mbachina.com，歡迎交流與合作。

贊
收藏
訂閱

備考交流

  • 【MBAChina 官方社群矩陣】
  • 涵蓋 199管理類(lèi)聯(lián)考備考 · 復(fù)試調(diào)劑 · 博士申請(qǐng) · 中外合辦學(xué) 四大板塊。
  • ??2027 MBA/MPA/MEM/MPAcc /EMBA聯(lián)考備考群
  • ??2026 管理類(lèi)聯(lián)考復(fù)試調(diào)劑群
  • ??博士項(xiàng)目交流群
  • ??中外合作辦學(xué)項(xiàng)目群
  • ?? 添加微信：MBAChina001
  • 備注【報(bào)考項(xiàng)目】，邀請(qǐng)您加入專(zhuān)屬交流群
免費(fèi)領(lǐng)取價(jià)值5000元MBA備考學(xué)習(xí)包 購(gòu)買(mǎi)管理類(lèi)聯(lián)考MBA/MPAcc/MEM/MPA大綱配套新教材

掃碼關(guān)注我們

  • 獲取報(bào)考資訊
  • 了解院校活動(dòng)
  • 學(xué)習(xí)備考干貨
  • 研究上岸攻略

最新動(dòng)態(tài)

    MBAChina 掃碼關(guān)注

    掃碼關(guān)注 MBAChina

    EMBA 掃碼關(guān)注

    掃碼關(guān)注
    EMBA

    大悟县| 武穴市| 富民县| 富源县| 南澳县| 北票市| 元阳县| 大姚县| 阳信县| 盐津县| 陵川县| 施秉县| 互助| 太康县| 精河县| 焉耆| 新兴县| 修文县| 新密市| 开鲁县| 安塞县| 沅江市| 芦山县| 封丘县| 阳信县| 建平县| 丹寨县| 彰武县| 拉萨市| 湟中县| 灌阳县| 库车县| 华容县| 霍州市| 龙井市| 寻甸| 安远县| 桐柏县| 定安县| 阳谷县| 绥棱县|