报告人：田国梁  南方科技大学教授

报告地点：统计学院213会议室

报告（一）

报告时间：12月20日下午14:40-15:40

报告题目：A new multivariate Laplace distribution based on the mixture of normal distributions (基于混合正态分布的一种新的多元拉普拉斯分布)

报告摘要: In this paper, we propose a new multivariate Laplace distribution from normal variance mixture models, called as Type II multivariate Laplace distribution. Unlike the multivariate Laplace distribution proposed by Eltoft (2006) that all components must have the same value for the mixing variate, the random components in the new distribution could have different value for its own mixing variate and are correlated only through the dependence structure of the normal random vector. Thus, it contains the multiplication of iid univariate Laplace distributions as a special case if the normal covariance matrix is diagonal. A tractable stochastic representation (SR) is used to derive the probability density function and other statistical properties. The maximum likelihood estimates of parameters via an ECM algorithm and the Bayesian methods are derived. Some simulation studies are conducted to evaluate the performance of the proposed methods. Applications in two real data sets indicate that the Type II multivariate Laplace distribution could have a better performance and is distinct from the original one.

报告（二）

报告时间：12月20日下午15:40-16:40

报告题目：Proportional inverse Gaussian distribution: A new tool for analyzing continuous proportional data (比例逆高斯分布: 一个分析连续比例数据的新工具)

报告摘要: Outcomes in the form of rates, fractions, proportions and percentages often appear in various fields. Existing beta and simplex distributions are frequently unable to exhibit satisfactory performances in fitting such continuous data. This paper aims to develop the normalized inverse Gaussian (N-IG) distribution proposed by Lijoi et al. (2005) as a new tool for analyzing continuous proportional data in (0, 1) and renames the N-IG as proportional inverse Gaussian (PIG) distribution. Our main contributions include: (1) To overcome the difficulty of an integral in the PIG density function, we propose a novel minorization-maximization (MM) algorithm via the continuous version of Jensen's inequality to calculate the maximum likelihood estimates of the parameters in the PIG distribution; (2) We also develop an MM algorithm aided by the gradient descent algorithm for the PIG regression model, which allows us to explore the relationship between a set of covariates with the mean parameter; (3) Both the robustness researches and the real data analyses show that the PIG distribution has the best robustness performance when comparing with the beta and simplex distributions in terms of the AIC, BIC and the p-value of the Kolmogorov-Smirnov test. In addition, bootstrap confidence intervals and testing hypothesis on symmetry of the PIG density are also presented. Simulation studies are conducted and the hospital stay data of Barcelona in 1988 and 1990 are analyzed to illustrate the proposed methods.

报告（三）

报告时间：12月20日下午16:40-17:40

报告题目：海内外升硕、升博面试及国内工作面试中的某些统计问题、数学问题和其它问题

报告摘要：在国内硕士推免与直博推免中，硕士国考后的复试中，博士招生的面试中，海外升硕升博及联合培养的远程面试中，面试老师将会提出各种各样的统计问题、数学问题、优化问题和其它问题，以考核申请者所掌握的统计与数学之基础知识和专业知识是否牢固，知识面是否宽广，语言表达是否流畅，逻辑思维是否清晰，反应能力是否敏捷，外文水平是否足够。如果平时学习不够扎实(或者尽管平时学习成绩很好但面试准备不够充分)，申请者的回答经常会令面试老师大失所望，进而影响主观打分并最终影响是否录取。本作者根据十多年以来，作为面试老师所获得的经验，归纳整理了几十个问题及相应的答案，希望对同学们和申请者在面试中有所帮助。

报告人简介：田国梁，现任南方科技大学数学系统计学正教授、博士生导师。田教授于1988年获得武汉大学统计学硕士学位、于1998年获得中国科学院应用数学研究所统计学博士学位。从1998至2002年, 他分别在北京大学概率统计系和美国田纳西州孟斐斯市的 St. Jude 儿童研究医院生物统计系从事博士后研究, 2002年至2008年他在美国马里兰大学Greenbaum 癌症中心任 Senior Bio-statistician。2008年至2016年他在香港大学统计及精算学系任副教授、博士生导师。田教授是国际统计学会 (ISI) 当选会员, 他担任 Computational Statistics & Data Analysis, Statistics and Its Interface 等四个国际统计学杂志的副主编。他主要的研究领域是生物统计, 社会统计和计算统计。目前的研究方向包括多元零膨胀计数数据分析、不完全分类数据分析和敏感性问题抽样调查。到目前为止，他在国际顶尖生物统计学期刊 Statistical Methods in Medical Research, Statistics in Medicine, Biometrics 发表论文13篇, 在其他统计学期刊发表论文80余篇。他在美国著名出版社 John Wiley & Sons 和 Chapman & Hall/CRC 出版英文专著3部, 且在中国的科学出版社出版中文专著1部和英文教科书1本。2017年度他的研究课题<<MM算法中的几类问题之研究及其应用>>获得国家自然科学基金面上项目的5A资助。