\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 21} (2011), 1065-1092

\vspace{3pc} \normalsize

\centerline{\large\bf NONPARAMETRIC FUNCTION ESTIMATION}
\vspace{2pt} \centerline{\large\bf   UNDER FOURIER-OSCILLATING
NOISE} \vspace{.25cm}
\author{Aurore Delaigle and Alexander Meister}
\vspace{.4cm} \centerline{\it University of
   Melbourne and
Universit\"at Rostock} \vspace{.55cm}  
\begin{quotation}
\noindent {\it Abstract:} In the popular deconvolution problem, the
goal is to estimate a curve $f$ from data that only allow direct
estimation of another curve $g$, the convolution of $f$ and a
so-called error density. Unlike the standard assumption in
deconvolution, we consider a more general setting where the
characteristic function of the error density can have zeros. This
problem is important as the characteristic functions of uniform
distributions, and more generally of many compactly supported
distributions, have some zeros. We propose a new nonparametric
deconvolution estimator, prove that its convergence rates are not
affected by the zeros if $f$ has a finite left endpoint, and we show
rate-adaptivity. We suggest data-driven bandwidth selectors and
examine their finite sample behaviour via simulated examples.\par
 \vspace{9pt} \noindent {\it Key words and phrases:}
Contaminated data, deconvolution, density estimation,
errors-in-variables regression, kernel smoothing, measurement
errors.\par
\end{quotation}
\end{document}
