保险和金融中的统计模型(Statistical Models in Insurance and Finance)
主讲人：彭亮(Liang Peng)，Georgia State University，教授，教育部长江学者讲座教授
内容简介：This course introduces students to some basic statistical models with applications in insurance and finance. In particular, we will use the statistical package R to show how to apply i) the simple linear regression to the capital asset pricing model; ii) the multiple linear regression to estimate fair market values; iii) the generalized linear model to predict the demand of term life insurance; iv) some data clustering techniques to variable annuity policies; and v) the principal component analysis to interest rate swaps.
课程 B
混合模型预测简介(Introduction to Mixed Model Prediction)
主讲人：蒋继明(Jiming Jiang)，University of California at Davis，教授，教育部长江学者讲座教授
内容简介：This course provides an introduction of mixed effects models and prediction methods based on such models. The mixed effects models include linear mixed models and generalized linear mixed models. The prediction methods include best prediction (in the sense of minimizing the mean squared prediction error), empirical best prediction, empirical best linear unbiased prediction, and classified mixed model prediction. Furthermore, measures of uncertainty and methods of obtaining those will be discussed. Applications of mixed model prediction techniques in areas such as business, economy, education and surveys will be discussed.
课程 C
主讲人：荆炳义(Bing-Yi Jing)，Hong Kong University of Science and Technology，教授，教育部长江学者讲座教授
C1：稀疏网络的社区检测(Community Detection of Sparse Network) 内容简介：Community detection for networks has been studied intensively in recent years. However, most methods focus on dense networks with littitiele study on sparse networks. In this talk, we shall investigate ways to detect communs for sparse networks. Simulation results will be given to illustrate the performance of the proposed methods.
C2：大数据时代统计学的作用(Role of Statistics in the Era of Big Data) 内容简介：Big data brought new opportunities and challenges. Statistics play an important role in this new era. In this talk, we will present some examples to demonstrate how statistics can help analyze complex data and make decisions.