应6163银河线路检测中心田波平教授的邀请，受国际合作处资助，印第安纳大学南本德分校关忠教授将于近日来访公司并做报告，欢迎感兴趣的师生参加。

报告1：2019年07月18 日14:00-15:30 格物楼503，

Title：Semiparametric maximum likelihood inference for nonignorable nonresponse with callbacks

Abstract：We model the nonresponse probabilities as logistic functions of the outcome variable and other covariates in the survey sampling study with callback. The identification aspect of this callback model is investigated. Semiparametric maximum likelihood estimators of the parameters in the response probabilities are proposed and studied. As a result, an efficient estimator of the mean of the outcome variable is constructed using the estimated response probabilities. Moreover, if a regression model for conditional mean of the outcome variable given some covariate is available, then we can obtain an even more efficient estimate of the mean of the outcome variable by fitting the regression model using an adjusted least squares method based on the estimated underlying distributions of the observed values. Simulation results show the proposed method is more efficient compared with some existing competitors. The method is applied to data from the Singapore Life Panel, a survey of health spending using a population-based sample of individuals aged 50-70 years, where non-response can may be related to health.

报告2：2019年07月18日 15:30 -17:00,格物楼503，

Title：Bernstein Polynomial Model for Nonparametric Multivariate Density

Abstract：In this paper, we study the Bernstein polynomial model for estimating the multivariate distribution functions and densities with bounded support. As a mixture model of multivariate beta distributions, the maximum (approximate) likelihood estimate can be obtained using EM algorithm. A change-point method of choosing optimal degrees of the proposed Bernstein polynomial model is presented. Under some conditions the optimal rate of convergence in the mean chi-squared-divergence of new density estimator is shown to be nearly parametric. The method is illustrated by an application to a real data set. Finite sample performance of the proposed method is also investigated by simulation study and is shown to be much better than the kernel density estimate but close to the parametric ones.

报告3：2019年07月20日09:00-10:30,格物楼503，

Title: Bernstein polynomial model for grouped continuous data.

Abstract: Grouped data are commonly encountered in applications. All data from a continuous population are grouped due to rounding of the individual observations. The Bernstein polynomial model is proposed as an approximate model in this paper for estimating a univariate density function based on grouped data. The coefficients of the Bernstein polynomial, as the mixture proportions of beta distributions, can be estimated using an EM algorithm. The optimal degree of the Bernstein polynomial can be determined using a change-point estimation method. The rate of convergence of the proposed density estimate to the true density is proved to be almost parametric by an acceptance-rejection argument used for generating random numbers. The proposed method is compared with some existing methods in a simulation study and is applied to the Chicken Embryo Data.

报告4：2019年07月20日, 10:40-12:10,格物楼503，

Title：Maximum Approximate Bernstein Likelihood Estimation in Some Semiparametric Models

Abstract: In this serial talks I will present the application of the maximum approximate Bernstein likelihood estimation(MABLE) in some semiparametric models such as Cox's proportional hazard regression model and the two-sample density ratio model. In the Cox model we consider the estimation based interval-censored data. Asymptotic results and simulation shows that the proposed MABLE not only gives a smooth maximum likelihood estimate of the baseline density and survival functions but also improves upon the estimates of regression coefficients. The new method overcomes the unidentify probability of the baseline distribution.

报告5：2019年07月20日15:00-16:30,格物楼503，

Title：The Applications of Maximum Approximate Bernstein Likelihood Estimation (MABLE)

Abstract: The density ratio model is equivalent to the univariate logistic regression model. The proposed MABLE of this model can also be applied to obtain estimates of density functions of two samples and he estimate of receiver operating characteristic curve. Simulation study was conducted to compare the new approach with existing ompetitors. Large sample properties of the proposed method are also given.

报告人简介：关忠，印第安纳大学南本德分校终身教授，6163银河线路检测中心统计学兼职博导，美国托莱多大学统计学博士，耶鲁老员工物统计系博士后，美国统计协会会员，美国数学协会会员。曾任黑龙江省概率统计协会秘书长，中国应用统计协会黑龙江省分会理事。在统计学顶级期刊Biometrika上发表1篇文章（独立作者），在生物统计顶级期刊Biometrics上发表1篇文章，在Statistica Sinica, Journal of Nonparametric Statistics, Journal of Statistical Planning and Inference, Canadian Journal of Statistics等国际一流期刊上发表文章30余篇。