报告题目：A class of new symmetric distributions based on scale mixtures of normal distribution and mean regression models by using N-EM and US algorithms

报告人：田国梁教授

报告时间：2026年6月4日 下午16:30-17:30

报告地点：格物楼528报告厅

报告摘要：In this paper, we propose a class of new symmetric distributions as candidates or alternatives to model continuous data on the real line, when existing distributions (such as normal, Student'st, two-parameter Laplace, logistic and so on) perform the data-fitting not well enough. Motivated by thestochastic representation(SR) of therandom variable(r.v.) following Student'st-distribution, the authors study thegeneral mixture of normal(Ge-N) distribution, which is defined by an SR involving a normal r.v.with zero mean and a positiver.v.with an arbitrary distribution. The Ge-N distribution includes the commonly-usedt, two-parameter Laplace, logistic distributions as three special cases and possesses a clear statistical interpretation. In the Ge-N framework, we first address the issue of identifiability of parameters, then develop three specific scale mixtures of normal distribution and corresponding mean regression models for analyzing continuous data with covariates. We apply thenormalized expectation-maximization(N-EM) algorithm aided by theupper-crossing/solution(US) algorithm to calculate maximum likelihood estimates of parameters. Simulation studies on model comparisons showed that the proposed three new models extend the application scope of existing models. Two real data sets are analyzed to illustrate the proposed

报告人简介：田国梁博士曾在美国马里兰大学从事医学统计研究六年,在香港大学统计与精算学系任副教授八年,从2016年6月至今在南方科技大学统计与数据科学系任教授、博士生导师。他目前的研究方向为EM/MM/US算法在统计中的应用、(0,1)区间上连续比例数据以及多元连续比例数据的统计分析、连续对称和连续非对称数据分析, 在国外发表 160 余篇 SCI 论文、出版 3 本英文专著、在科学出版社出版英文教材2本。他曾是四个国际统计期刊的副主编, 目前是国际统计期刊 SII (Statistics and Its Interface) 的副主编。主持国家自然科学基金面上项目二项、主持深圳市稳定支持面上项目一项、参加国家自然科学基金重点项目一项。