统计学系系列讲座之245期

时间：2016年12月20日（星期二）下午14:00—15:00

地点：史带楼302室

主持人：黎德元 教授 复旦大学管理学院统计学系

主题：Efficient estimation in semivarying coefficient models for longitudinal data

主讲人：Prof. Ming-Yen Cheng

National Taiwan University

摘要：In semivarying coefficient modeling of longitudinal/clustered data, of primary interest is usually the parametric component which involves unknown constant coefficients. First we study semiparametric efficiency bound for estimation of the constant coefficients in a general setup. It can be achieved by spline regression using the true within-subject covariance matrices, which are often unavailable in reality. Thus we propose an estimator when the covariance matrices are unknown and depend only on the index variable. To achieve this goal, we estimate the covariance matrices using residuals obtained from a preliminary estimation based on working independence and both spline and local linear regression. Then, using the covariance matrix estimates, we employ spline regression again to obtain our final estimator. It achieves the semiparametric efficiency bound under normality assumption and has the smallest asymptotic covariance matrix among a class of estimators even when normality is violated. Our theoretical results hold either when the number of within-subject observations diverges or when it is uniformly bounded. In addition, the local linear estimator of the nonparametric component is superior to the spline estimator in terms of numerical performance. The proposed method is compared with the working independence estimator and some existing method via simulations and application to a real data example.

统计学系

2016-12-15