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\footnotesize
\noindent Statistica Sinica {\bf 15}(2005), 969-980

\vspace{3pc} \normalsize
\centerline{\large\bf BAYESIAN BOOTSTRAP ANALYSIS OF DOUBLY}
\vspace{2pt}
\centerline{\large\bf CENSORED DATA USING GIBBS SAMPLER}
\vspace{.25cm}
\author{Yongdai Kim, Jaeyong Lee and Jinseog Kim}
\vspace{.4cm}
\centerline{\it Seoul National University}
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\begin{quotation}
\noindent {\it Abstract:}
The Bayesian bootstrap for doubly censored data is constructed from
the empirical likelihood perspective, and a Gibbs sampler algorithm
is proposed for evaluating the Bayesian bootstrap posterior. The
proposed Bayesian bootstrap posterior is shown to be the limit of
the nonparametric posteriors with Dirichlet process priors as the
prior information vanishes, and to be equivalent to the weighted
bootstrap on the observables. A small simulation study shows that
the proposed Bayesian bootstrap estimator compares favorably with
the nonparametric maximum likelihood estimator; furthermore its
asymptotic properties are studied.\par
\vspace{9pt}
\noindent {\it Key words and phrases:}
Bayesian bootstrap, doubly censored data, empirical likelihood, survival model.\par
\end{quotation}

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