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2018概率统计及其应用系列报告之五(胡涛、周洁)

  发布日期:2018-05-17  浏览量:777


系列报告一

报告题目: Non-parametric models for joint probabilistic distributions of wind speed and direction data

: 胡涛首都师范大学澳门赌搏网站大全副教授

报告摘要:Two non-parametric models, namely the non-parametric kernel density (NP-KD) and non-parametric JW (NP-JW) models, are proposed for joint probabilistic modeling of wind speed and direction distributions. In the NP-KD model, a novel bivariate kernel density function, which could consider the characteristics of both wind direction (angular) and speed (linear) data, is firstly constructed and the optimal bandwidth is selected globally through two cross-validation (CV) methods. In the NP-JW model, the univariate Gaussian and von Mises kernel density functions are, respectively, utilized to fit the wind speed and direction data. The estimated wind speed and direction distributions are used to form the joint distribution according to the JW model. Several classical parametric models, including the AG, Weibull, Rayleigh, JW-TNW and JW-FMN models, are also introduced in order for comparisons with the proposed non-parametric models. By conducting various tests on the real hourly wind speed and direction data, the goodness of fit of both parametric and non-parametric models is compared and evaluated in detail. It is shown that the non-parametric models (NP-KD, NP-JW) generally outperform the parametric models (AG,Weibull, Rayleigh,JW-TNW,JW-FMN) and have more robust performance in fitting the joint speed and direction distributions. Among the two non-parametric models, the NP-KD model has better performance in fitting joint distribution, while the NP-JW model has higher accuracy in fitting the marginal speed (or direction) distributions.

报告时间: 2018521(周一)  9:30-10:30

报告地点: 磬苑校区澳门赌搏网站大全H306

 

 

 

系列报告二

报告题目: Efficient fused learning for distributed imbalanced datasets

: 周洁首都师范大学澳门赌搏网站大全讲师

报告摘要:Imbalanced data occurs when we analyze  rare events. In the era of bigdata, we may have enough observations but are distributed in different datasets which cannot be combined. Doing estimation on each dataset and taking average is a recommended method for analysis of distributed data but may result in unstable estimations in   case of imbalanced data. We propose a fused learning approach in this paper which utilizes  the cases on all dataset to obtain a stable estimation on each dataset. The asymptotic properties and efficiency considerations are established. Simulation studies are conducted to evaluate the finite sample behaviors of the proposed estimators, which show that the proposed estimator works as efficient as the oracle estimator obtained basing on all datasets in the considered situations.

报告时间: 2018521(周一)  10:30-11:30

报告地点: 磬苑校区澳门赌搏网站大全H306

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2018517

专家概况:

        涛,首都师范大学澳门赌搏网站大全副教授,硕导。2009年毕业于北京师范大学澳门赌搏网站大全,获概率论与数理统计专业博士学位,美国University of Missouri 统计系博士后。20093月至201212月先后在新加坡国立大学统计与应用概率系、南洋理工大学数学与物理学院任Research AssistantResearch Fellow。主持或参与多项国家项目,在Journal of the American Statistical AssociationBiometrikaStatistica SinicaStatistics and its Interface等杂志上发表论文20余篇。

周洁,首都师范大学澳门赌搏网站大全讲师,2012年毕业于中科院数学与系统科学研究所,获统计学专业博士学位。2012-2013年在美国华盛顿大学做博士后工作。研究领域包括半参和非参数统计、生存分析、复发事件和纵向数据分析、统计计算等。主持或参与多项国家项目,在Journal of the American Statistical AssociationStatistica SinicaBiometrics等杂志上发表论文20余篇。

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