JSM 2011 Online Program
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Activity Details
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| CE_20C | Tue, 8/2/2011, 8:30 AM - 5:00 PM | HQ-Poinciana Salon 1 | |
| Semiparametric Theory and Missing Data — Continuing Education Course | |||
| ASA , Biometrics Section | |||
| Instructor(s): Anastasios Tsiatis, North Carolina State University | |||
| Semiparametric models, which involve both a parametric and nonparametric component, have gained great popularity because of their flexibility and applicability to many statistical problems. Considerable research on the theoretical properties of estimators for parameters in semiparametric models has been carried out. Missing data is a common problem. Most of the research on missing data has focused on parametric models. In a seminal paper by Robins, Rotnitzky, and Zhao (1994), it was shown how proper inference can be made on parameters in both parametric and semiparametric models when some of the data are missing. This was accomplished by introducing the notion of an augmented inverse probability weighted complete-case (AIPWCC) estimator. The course will be a full-day short course broken down into two distinct sessions. The morning session will introduce the theory and methods for semiparametric models assuming there are no missing data. Using ideas developed in the morning session, the afternoon session will discuss how to extend these ideas to missing data problems and show how this leads to the AIPWCC estimators. The course requires that participants have taken an advanced course in inference and probability and have a good understanding of large sample theory. | |||
2011 JSM Online Program Home
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