\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 19} (2009), 1665-1682

\vspace{3pc}
\normalsize
 \centerline{\large\bf SERIES REPRESENTATIONS FOR MULTIVARIATE}
\vspace{2pt}
 \centerline{\large\bf GENERALIZED GAMMA PROCESSES VIA}
\vspace{2pt}
 \centerline{\large\bf A SCALE INVARIANCE PRINCIPLE}
\vspace{.25cm}
 \author{Hemant Ishwaran and Mahmoud Zarepour}
\vspace{.4cm}
 \centerline{\it Cleveland Clinic and University of Ottawa}
\vspace{.55cm}
\begin{quotation}
\noindent {\it Abstract:} We introduce a scale invariance property
for Poisson point processes and use this property to define a series
representation for a correlated bivariate gamma process. This
approach is quite general and can be used to define other types of
multidimensional L\'evy processes with given marginals.  Some
important special cases are bivariate $G$-processes, bivariate
variance gamma processes and multivariate Dirichlet processes. Using
the scale invariance principle we show how to construct simple
approximations to these multivariate processes. \par
 \vspace{9pt}
 \noindent
{\it Key words and phrases:} Correlated process, easure,
$G$-measure.
\par
\end{quotation}

\end{document}
