中国人民大学信息学院邀请Xin(Cynthia) Tong作了一场题为“Robust Frequentist versus Bayesian Methods for Growth Curve Modeling(强大的频率论与贝叶斯方法生长曲线造型)”的讲座，信息产业是21世纪的朝阳产业，也是21世纪我国国民经济的支柱产业。信息产业需要计算机科学与技术、信息系统与信息管理、数学基础与理论等各方面的专业人才和复合人才。中国人民大学信息学院正是培养信息领域高素质专业人才的基地。讲座的主要内容是：

　　生长曲线模型经常用于研究生长并改变在社会，行为和教育科学现象和是的基本工具之一用于处理具有纵向的数据。许多研究已经证明，在实践中正态分布的数据是相当的异常，特别是当数据被纵向收集。估计模型没有考虑数据的非正态性可能导致效率低下，甚至不正确的参数估计。因此，稳健的方法成为增长曲线造型非常重要的。在现有的稳健的方法，从频率论角度两级可靠的方法(元与张，2012)和贝叶斯角度半参数贝叶斯方法(桐，2014年)，是有希望的。本研究的目的是通过样品大小，测量场合数，人口分布，异常值的存在时，潜之间协方差的变化的条件，比较通过蒙特卡罗仿真研究了两种方法的性能上的线性生长曲线模型，截距和斜率，以及测量误差方差。仿真结果表明，这两种方法提供更准确和精确的参数估计值比传统的增长曲线造型当正常的假设侵犯。当数据来自正态分布的混合半参数贝叶斯方法执行得更好。如果数据是正常的，这两种方法估计模型以及传统的生长曲线造型。还提供基于从1997年青年队列的国家纵向调查数据集进行分析的实时数据的例子来说明这两个强大的方法的应用。

　　原文：Growth curve models are often used to investigate growth and change phenomena in social, behavioral, and educational sciences and are one of the fundamental tools for dealing with longitudinal data. Many studies have demonstrated that normally distributed data in practice are rather an exception, especially when data are collected longitudinally. Estimating a model without considering the nonnormality of data may lead to inefficient or even incorrect parameter estimates. Therefore, robust methods become very important in growth curve modeling. Among the existing robust methods, the two-stage robust approach (Yuan & Zhang, 2012) from the frequentist perspective and the semiparametric Bayesian approach (Tong, 2014) from the Bayesian perspective are promising. The purpose of this study is to compare the performance of the two approaches through a Monte Carlo simulation study on a linear growth curve model, by varying conditions of sample size, number of measurement occasions, population distribution, existence of outliers, covariance between the latent intercept and slope, and variance of measurement errors. Simulation results show that both approaches provide more accurate and precise parameter estimates than the traditional growth curve modeling when the normal assumption is violated. The semiparametric Bayesian approach performs better when data come from a mixture of normal distributions. If data are normal, the two approaches estimate the model as well as the traditional growth curve modeling. A real-data example based on the analysis of a dataset from the National Longitudinal Survey of Youth 1997 Cohort is also provided to illustrate the application of the two robust approaches.