\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\author{\centerline}

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

\footnotesize
\noindent Statistica Sinica {\bf 15}(2005), 981-1001

\vspace{3pc} \normalsize
\centerline{\large\bf MULTIVARIATE REGRESSION S-ESTIMATORS FOR}
\vspace{2pt}
\centerline{\large\bf ROBUST ESTIMATION AND INFERENCE}
\vspace{.25cm}
\author{Stefan Van Aelst and Gert Willems}
\vspace{.4cm}
\centerline{\it Ghent University and University of Antwerp}
\vspace{.55cm}

\begin{quotation}
\noindent {\it Abstract:}
In this paper we consider S-estimators for multivariate regression.
We study the robustness of the estimators in terms of their breakdown
point and influence function. Our results extend results on S-estimators
in the context of univariate regression and multivariate location and scatter.
Furthermore we develop a fast and robust bootstrap method for the multivariate
S-estimators to obtain inference for the regression parameters. Extensive simulation
studies are performed to investigate finite-sample properties. The use of the
S-estimators and the fast, robust bootstrap method is illustrated on some data.\par
\vspace{9pt}
\noindent {\it Key words and phrases:}
Breakdown point, influence function, robust bootstrap.\par
\end{quotation}

\end{document}
