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  \textbf{\textsc{STANFORD UNIVERSITY}}\\[5pt]
  \textbf{\textsc{DEPARTMENT OF STATISTICS}}\\[5pt]
  \Large{\textbf\textsc{{DEPARTMENTAL SEMINAR}}}
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\begin{center}
  4:15 p.m., Tuesday, April 26, 2005\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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\begin{center}
  \textsl{Jiming Jiang}\\
  Department of Statistics\\
  University of California, Davis\\
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  \textbf{Iterative Estimating Equations}
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We propose an iterative estimating equations procedure for
analysis of longitudinal data. We show that, under very mild conditions,
the probability that the procedure converges at an exponential rate tends
to one as the sample size increases. Furthermore, we show that the limiting
estimator is consistent and asymptotically efficient, as expected.
The method applies to semiparametric regression models with unspecified
covariances among the observations. In the special case of linear models,
the procedure reduces to iterative weighted least squares. An extension of
the method incorporating data that is missing informatively is discussed.
Finite sample performance of the procedure is studied using a simulated
example, and compared with other methods. A numerical example from a
medical study is considered to illustrate the application of the method.

This work is joint with YouGan Wang, National University of Singapore
and Yihui Luan, Shandong University, PRC.

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