中國人民大學(xué)信息學(xué)院邀請Xin(Cynthia) Tong作了一場題為“Robust Frequentist versus Bayesian Methods for Growth Curve Modeling(強大的頻率論與貝葉斯方法生長曲線造型)”的講座，信息產(chǎn)業(yè)是21世紀(jì)的朝陽產(chǎn)業(yè)，也是21世紀(jì)我國國民經(jīng)濟的支柱產(chǎn)業(yè)。信息產(chǎn)業(yè)需要計算機科學(xué)與技術(shù)、信息系統(tǒng)與信息管理、數(shù)學(xué)基礎(chǔ)與理論等各方面的專業(yè)人才和復(fù)合人才。中國人民大學(xué)信息學(xué)院正是培養(yǎng)信息領(lǐng)域高素質(zhì)專業(yè)人才的基地。講座的主要內(nèi)容是：

　　生長曲線模型經(jīng)常用于研究生長并改變在社會，行為和教育科學(xué)現(xiàn)象和是的基本工具之一用于處理具有縱向的數(shù)據(jù)。許多研究已經(jīng)證明，在實踐中正態(tài)分布的數(shù)據(jù)是相當(dāng)?shù)漠惓�，特別是當(dāng)數(shù)據(jù)被縱向收集。估計模型沒有考慮數(shù)據(jù)的非正態(tài)性可能導(dǎo)致效率低下，甚至不正確的參數(shù)估計。因此，穩(wěn)健的方法成為增長曲線造型非常重要的。在現(xiàn)有的穩(wěn)健的方法，從頻率論角度兩級可靠的方法(元與張，2012)和貝葉斯角度半?yún)��?shù)貝葉斯方法(桐，2014年)，是有希望的。本研究的目的是通過樣品大小，測量場合數(shù)，人口分布，異常值的存在時，潛之間協(xié)方差的變化的條件，比較通過蒙特卡羅仿真研究了兩種方法的性能上的線性生長曲線模型，截距和斜率，以及測量誤差方差。仿真結(jié)果表明，這兩種方法提供更準(zhǔn)確和精確的參數(shù)估計值比傳統(tǒng)的增長曲線造型當(dāng)正常的假設(shè)侵犯。當(dāng)數(shù)據(jù)來自正態(tài)分布的混合半?yún)��?shù)貝葉斯方法執(zhí)行得更好。如果數(shù)據(jù)是正常的，這兩種方法估計模型以及傳統(tǒng)的生長曲線造型。還提供基于從1997年青年隊列的國家縱向調(diào)查數(shù)據(jù)集進(jìn)行分析的實時數(shù)據(jù)的例子來說明這兩個強大的方法的應(yīng)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.