\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
\def\mbf#1{\mbox{\boldmath $#1$}}
\def\author{\centerline}

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

\footnotesize
\noindent Statistica Sinica {\bf 17}(2007), 571-597

\vspace{3pc} \normalsize
\centerline{\large\bf A ROBUST SEQUENTIAL BAYESIAN METHOD FOR}
\vspace{2pt}
\centerline{\large\bf IDENTIFICATION OF DIFFERENTIALLY}
\vspace{2pt}
\centerline{\large\bf EXPRESSED GENES}
\vspace{.25cm}
\author{Faming Liang$^1$, Chuanhai Liu$^2$ and Naisyin Wang$^1$}
\vspace{.4cm}
\centerline{\it $^1$Texas A{\rm \&}M University and $^2$Purdue University}
\vspace{.55cm}
\begin{quotation}
\noindent {\it Abstract:} A DNA microarray experiment simultaneously
measures the expression levels of thousands of genes. An important
question is to identify genes that express differentially between
two types of tissues or at different experimental conditions. Since
large numbers of genes are compared simultaneously, simple use of
significance tests can easily lead to false positive findings. We
propose a sequential procedure for estimating the empirical null
distribution of multiple hypothesis testing and apply the procedure
to identify differentially expressed genes in microarray
experiments. Our procedure can be viewed as a new method to estimate
the $q$-value proposed by Storey (2002). The key intuition is to
obtain an estimate of the null distribution that is robust to the
observations from the alternative distribution. Technically, we
borrow strength from the missing data literature so that we can
avoid estimating the density function corresponding to
differentially expressed genes nonparametrically, but can focus on
estimating the null density. Numerical comparisons between our
method and Storey's original method were conducted in simulated and
real data examples. The numerical results show that our procedure
outperforms the originally estimated $q$-values in almost all
scenarios. \par \vspace{9pt} \noindent {\it Key words and phrases:}
False discovery rate, Markov chain Monte Carlo, microarray data
analysis, missing data, multiple hypothesis testing. \par
\end{quotation}

\end{document}
