\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 $#1$}}


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

\footnotesize
\noindent Statistica Sinica {\bf 13}(2003), 555-570
\vspace{3pc} \normalsize
\centerline{\large\bf SIZE AND POWER CONSIDERATIONS FOR TESTING}
\vspace{2pt}
\centerline{\large\bf LOGLINEAR MODELS USING $\phi$-DIVERGENCE TEST}
\vspace{2pt}
\centerline{\large\bf STATISTICS}
\vspace{.25cm}
\centerline{Noel Cressie$^1$, Leandro Pardo$^2$ and Maria del Carmen Pardo$^2$}
\vspace{.4cm}
\centerline{\it $^1$The Ohio State University and $^2$Complutense University of
Madrid}
\vspace{.55cm}
\begin{quotation}
\noindent {\it Abstract:}
In this article, we assume that categorical data are distributed according
to a multinomial distribution whose probabilities follow a loglinear model.
The inference problem we consider is that of hypothesis testing in a
loglinear-model setting. The null hypothesis is a composite hypothesis
nested within the alternative. Test statistics are chosen from the general
class of $\phi$-divergence statistics. This article collects together the
operating characteristics of the hypothesis test based on both asymptotic
(using large-sample theory) and finite-sample (using a designed simulation
study) results. Members of the class of power divergence statistics are
compared, and it is found that the Cressie-Read statistic offers an
attractive alternative to the Pearson-based and the likelihood ratio-based
test statistics, in terms of both exact and asymptotic size and power. \par
\vspace{9pt}
\noindent {\it Key words and phrases:}
Chi-squared distribution, contiguous alternatives, multinomial distribution,
nested hypotheses, noncentral chi-squared distribution, power-divergence
statistic.\par
\end{quotation}
\end{document}

