\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}
\def\on{\o(n^{-1})}
\def\opn{\o_p(n^{-1})}
\def\On{\O(n^{-1})}
\def\Opn{\O_p(n^{-1})}
\def\orn{\o(n^{-1/2})}
\def\oprn{\o_p(n^{-1/2})}
\def\Orn{\O(n^{-1/2})}
\def\Oprn{\O_p(n^{-1/2})}
\setcounter{page}{1}
\begin{document}
\pagestyle{empty}

\footnotesize
\noindent Statistica Sinica {\bf 15}(2005), 1081-1096

\vspace{3pc} \normalsize
\centerline{\large\bf CONFIDENCE INTERVALS FOR THE MEAN VALUE OF}
\vspace{2pt}
\centerline{\large\bf RESPONSE FUNCTION IN GENERALIZED}
\vspace{2pt}
\centerline{\large\bf  LINEAR MODELS}
\vspace{.25cm}
\author{Jin Xu and Arjun K. Gupta}
\vspace{.4cm}
\centerline{\it Bowling Green State University}
\vspace{.55cm}

\begin{quotation}
\noindent {\it Abstract:}
Confidence intervals for the mean value
of the response function in generalized linear models are proposed
to improve the accuracy of the approximation when the distribution
of response is nonnormal and the sample size is moderate. The
correction will give the approximation error up to order of $\orn$
for the one-sided case and of $\on$ for the two-sided case. Monte
Carlo studies are given to compare our results with the classical
ones.\par
\vspace{9pt}
\noindent {\it Key words and phrases:}
Confidence intervals, Edgeworth expansion, Skorohod representation, coverage
probability, generalized linear models.\par
\end{quotation}

\end{document}
