\documentclass[10pt]{book}
\usepackage{array}
\textwidth=31.9pc
\textheight=46.5pc
\oddsidemargin=1pc
\evensidemargin=1pc
\headsep=15pt
%\headheight=.2cm
\topmargin=.6cm
\parindent=1.7pc
\parskip=0pt



\setcounter{page}{1}
\begin{document}
\pagestyle{empty}

\footnotesize
\noindent Statistica Sinica {\bf 13}(2003), 189-206
\vspace{3pc} \normalsize

\centerline{\large\bf BAYESIAN COMPUTATION FOR CONTINGENCY TABLES}
\vspace{2pt}
\centerline{\large\bf WITH INCOMPLETE CELL-COUNTS}
\vspace{.25cm}
\centerline{Guo-Liang Tian$^{*,\dag}$, Kai Wang Ng$^\ddag$ and Zhi Geng$^*$}
\vspace{.4cm}
\centerline{\it $^*$Peking University, $^\ddag$The University of Hong Kong}
\centerline{\it and $^\dag$St.$\!$ Jude Children's Research Hospital}

\vspace{.55cm}
\begin{quotation}
\noindent {\it Abstract:}
This article studies Bayesian analysis of contingency tables (or multinomial
data) where the cell counts are not fully observed due to reasons such as
nonresponse and misclassification, and derives the posterior distributions of
the unknown cell probabilities in terms of various types of generalized
Dirichlet distributions. For some special situations such as grouped and
nested Dirichlet distributions, the posterior means of the unknown cell
probabilities can be obtained in closed form by using inverse Bayes formulae
and/or stochastic representation. When closed-form expressions do not exist,
we suggest using importance sampling  with a feasible proposal density to
approximately compute the posterior quantities, and propose a procedure for
choosing an effective proposal density. Applications are illustrated by sample
surveys with nonresponse, crime survey data, death penalty attitude data, and
misclassified multinomial data.\par
\vspace{9pt}
\noindent {\it Key words and phrases:}
Bayesian inference, grouped and nested Dirichlet distributions, incomplete
data, inverse Bayes formulae, stochastic representation.\par
\end{quotation}
\end{document}
