Statistics Colloquium
November 11 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

Structured Nonparametric Methods for Estimation, Prediction and Tracking with Longitudinal Data

Colin O. Wu
Office of Biostatistics Research
National Heart, Lung and Blood Institute
National Institutes of Health

Longitudinal analysis has three important objectives in biomedical studies: (a) estimating the time-varying population-average and subject-specific covariate effects on the outcome process of interest; (b) predicting the future subject-specific outcome trajectories; (c) evaluating the tracking abilities of important risk factors and health outcomes. Because longitudinal data (which is often referred as functional data) consist of repeatedly measured outcome and covariate processes over time, they can be used to accomplish the above three objectives simultaneously.

Popular parametric methods for longitudinal analysis, such as the generalized mixed-effects models, are often too restrictive and unrealistic for real applications because of their modeling assumptions. On the other hand, nonparametric models without any structural assumptions could be computationally infeasible and difficult to interpret. We present in this talk some structured nonparametric methods to accomplish the above three objectives, namely estimation, prediction and tracking, based on a class of nonparametric mixed-effects models. Our methods, which use either local kernel-type smoothing or global smoothing via B-splines, have the appropriate model flexibility and computational feasibility, and are useful to answer many scientific questions which could not be properly addressed by parametric or unstructured nonparametric regression models. We demonstrate the application of our methods through a long-term epidemiological study of pediatric cardiovascular risk factors and a series of simulation studies. Asymptotic developments of our methods suggest that the convergence rates of our smoothing estimators depend on the number of subjects as well as the numbers of repeated measurements.

Colin O. Wu is senior Mathematical Statistician at the National Heart, Lung and Blood Institute, National Institutes of Health (NHLBI/NIH). He is also Adjunct Professor at Georgetown University and The George Washington University. Dr. Wu received Ph.D. in statistics from the University of California, Berkeley. His professional activities include serving as board and committee members for Statistics in Medicine, Biometrics, Department of Veterans Affairs, the Association for the Advancement of Medical Instrumentation (AAMI), among others. He is Elected Member of the International Statistical Institute, and Fellow of the American Statistical Association. He has published in statistics, biostatistics and medical journals.

All statistics graduate students are expected to attend.


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Department of Statistics
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