\documentclass[12pt]{article}
\usepackage{psfig}
\hoffset=-0.875in
\advance\topmargin by -0.6truein
\oddsidemargin=1.300truein
\evensidemargin=1.300truein
\textwidth=6.0truein
\textheight=8.4truein
\def\mbit#1{\hbox{\bfseries\textit{#1}}}
\def\binom#1#2{{{#1}\choose{#2}}}
\begin{document}
\renewcommand{\baselinestretch}{1.6}
\baselineskip=1.6\normalbaselineskip
% title
\noindent{\Large \bf A TEMPLATE FOR A CONTRIBUTION}
\vspace*{0.3in}

% short abstract
\noindent
\begin{quote}
{\bf Abstract.~}%--------------------------------------------
This is a short abstract for the contribution.
The size of the abstract should be about one or two paragraphs describing clearly what
the reader should expect in the main body of the contribution. It should not cite
any references.
\end{quote}

\vspace*{0.1in} \noindent {\bf Keywords and Phrases.} %----------------------------------
Template, ESS2, Author's Guide.

\vspace*{0.1in} \noindent {\bf AMS Subject Classification.} %----------------------------------
Primary: 60T42, 78J12;
Secondary: 44R44, 43B43, 01A01


\vspace*{0.2in}
\noindent
{\large \bf SECTION}

This is the body of the contribution.  In this template we provide all the elements that a contribution might have:  tables, figures, sections, subsections, examples, remarks, etc.  We have avoided extensive use of \LaTeX macros since many machines out there do not share them and are restricted only to the basic ones, such as ``psfig", for example.
The authors  should not create their own \LaTeX macros, as well.

Note that the bibliography is divided into two parts: ``References'' (cited) and
``Further  Reading'' list. We will give a format for various reference items: books,
journal articles, PhD theses, manuscripts, etc.

For example,  the book \cite{rf:1} covers the area of robust time/frequency
inference, the articles \cite{rf:2,rf:3}
are unrelated. It is expected that the  manuscript \cite{rf:4} will be published shortly.

\vspace*{0.2in}
\noindent {\bf Subsection}

We provide examples of a Definition and a Theorem.
A Result, Corollary, Description, Property, etc., can be given in
a similar manner.
%% DEFINITION %%

\vspace*{0.2in}
\noindent {\bf Definition 1.}
Kolmogorov complexity of a binary word $x$, with respect to a partially
recursive function $F$, is
\begin{eqnarray*}
K_F(x) = \left\{ \begin{array}{ll}
                 \min_p \ell(p) &  ~F(p) = x \\
                 \infty  & ~(\forall p) ~F(p) \ne  x
                 \end{array}
         \right.
\end{eqnarray*}


%% THEOREM %%
\vspace*{0.2in}
\noindent {\bf Theorem 1.}
For testing
\begin{eqnarray*}
H_0: \tau= 0 ~~~v.s.  ~~~H_1: \tau \ne  0,
\end{eqnarray*}
the Bayes factor (in favor of $H_1$) is
\begin{eqnarray}
B=\frac{|\Sigma_0|^{\frac{1}{2}} }{|\Sigma_1|^{\frac{1}{2}} }
e^{-\frac{1}{2} y' (\Sigma_1^{-1}-\Sigma_0^{-1}) y },
\label{eq:bayes}
\end{eqnarray}
where the sample $\Sigma_0=\frac{\sigma^2}{n} I + \sigma_1^2 J$
 and $\Sigma_1=(\frac{\sigma^2}{n}+\sigma_2^2) I + \sigma_1^2 J.$




\vspace*{0.2in} \noindent
An example of a table is provided next. Tables and figures should be deferred
to the end of the contribution with  a pointer in the body where they should be
located.

\vspace{0.1in}
\noindent {\bf Example 1.} The following data were extracted from a larger study \cite{rf:3}
by Brower,
on  speciation in a group of swallowtail butterflies.
Morphological measurements are in millimeters coded $\times$ 8,
($Y_1$ - length of 8th tergile, $Y_2$ - length of superuncus), see Table 1.

%----------------
\vspace{0.1in}
\noindent \fbox{Table 1 About Here.}
\vspace{0.1in}
%------------------

The correlation coefficients separately for each species are
$r_1=-0.11 $ (for {\it P. multicaudatus})
and
$r_2 =  0.176 $ (for {\it P. rutulus})

Rules with a more desirable shape result from the
$\epsilon$--contaminated priors, with a point mass at zero.
 When
\begin{eqnarray}
\label{eq:rule}
[\theta|\epsilon] \sim  \epsilon \delta_0 +
(1-\epsilon) {\cal DE}(0, \tau),
\end{eqnarray}
the marginal is
\begin{eqnarray*}
 {d} \sim
m^*(d) = \epsilon {\cal DE}(0,\frac{1}{\sqrt{2 \mu}}) +
(1-\epsilon) m(d)
 \end{eqnarray*}
 and the Bayes rule is:
\[
\delta^*(d)=\frac{ (1-\epsilon)~ m(d)~ \delta(d) }{(1-\epsilon)~
m(d) + \epsilon
 ~{\cal DE}\left(0, \frac{1}{\sqrt{2 \mu}} \right) }.
\]
This rule is depicted in Fig. 1 for some specific
values of hyperparameters. Also, this refers to equation (\ref{eq:rule}).
Equations (\ref{eq:bayes}) and (\ref{eq:rule}) are unrelated.

%----------------
\vspace{0.1in}
\noindent \fbox{Figure 1 About Here.}
\vspace{0.1in}
%------------------

\noindent
{\bf Remark 1.} In his work {\it Regression Toward Mediocrity in Hereditary Stature}  (see
Galton (1885), in \cite{rf:5}, Vol. II, pp. 371-372.)
 Galton concentrated on the prediction of physical traits of offspring from knowledge of the parents'
physical traits. A general finding of his work was that children tend to "regress toward the mean".
For example, taller than average parents tended to have children that
were shorter than they. Likewise, shorter than average parents tended to have children
taller than they.

\vspace*{0.1in}
\noindent {\bf Remark 2.}
In the course of time the word {\it regression} became a synonym for the
statistical study of relationship between two or more variables.

Author(s) name(s) are the end of the contribution, after Acknowledgements,
flushed right.

\vspace*{0.1in}
\noindent {\bf Acknowledgements.} The author's work was prepared
under  NSF  grant
at Super-Duper University DMS-02-12223.

\vspace{0.1in}
\begin{flushright}
  { James C. Dunlop}\\[-0.05in]
  { Statistics Department}\\[-0.15in]
  {Super-Duper University}\\[-0.15in]
  { Howde Hall, 456 University Dr.}\\[-0.15in]
  { Town, GA 30066, USA}\\
  {\tt james.dunlop@stat.s-du.edu}
  \end{flushright}


\begin{thebibliography}{99}

\bibitem{rf:4} Antoniadis, A., Bigot, J., and Sapatinas, T.
 (2001).
Wavelet estimators in nonparametric regression: Description and
simulative comparison. Technical report IMAG, France. \newline { \tt
http://www-lmc.imag.fr/SMS/software/wavden/wavden.pdf.zip.}

\bibitem{rf:3} Brower, L. P. (1959). Speciation in butterflies of the
{\it Papilio glaucus} group. I Morphological Relationships
and hybridizations. {\it Evolution} 13, 40-63.


\bibitem{rf:1} Flandrin, P. (1999).
{\it Time-Frequency/Time-Scale Analysis.}
Academic Press, San Diego, CA.

\bibitem{rf:2}   Kolmogorov, A. N. (1941). The local structure
of turbulence in incompressible viscous fluid for very large
Reynolds numbers, {\it Dokl. Akad. Nauk. SSSR,} {\bf 30},
301--305.

\bibitem{rf:5}  Pearson, K. (1914, 1924, 1930a, 1930b). {\it The Life,
 Letters and Labours of Francis Galton.} Vol. I, II, IIIA, IIIB. Cambridge University Press,
 Cambridge, UK.
\end{thebibliography}
%
\vspace*{0.1in} \noindent
{\Large \bf Further Reading}
\vspace*{0.1in}

 \parindent -0.3truein
 \leftskip 0.3truein
 \parskip 4pt

Galton, F. (1869). {\it Hereditary Genius.} Macmillan, London.

Stigler, S. M. (1986). {\it A History of Statistics: The Measurement of Uncertainty Before 1900.}
Belknap, Harward University Press, Cambridge, MA.

Kumar, P. and  Foufoula-Georgiou, E. (1994).
 Wavelet analysis in geophysics: An introduction, In
{\it Wavelets in Geophysics}, Eds Foufoula-Georgiou and Kumar,
 Academic Press, 1--43.

 \leftskip 0 truein
 \parindent 0 truein


\vspace*{0.1in}
\noindent {\large \bf Related Entries:} ESS2, \LaTeX template, Wiley

%============ TABLES AND FIGURES=====================================================
\newpage


\begin{table}[h]
\centering
 \label{tbl1}
\caption{Papilio m. and Papilio r.}
\vspace{0.1in}
\begin{tabular}{|l|ll|ll|ll|ll|}
\hline
Species & $Y_1$ & $Y_2$ & $Y_1$ & $Y_2$& $Y_1$ & $Y_2$& $Y_1$ & $Y_2$ \\
\hline \hline
{\it Papilio} & 24 & 14 & 21 & 15 & 20 & 17.5 & 21.5 & 16.5 \\
{\it multicaudatus}& 21.5 & 16 & 25.5 & 16 & 25.5 & 17.5 & 28.5 & 16.5 \\
   & 23.5 & 15 & 22 & 15.5 & 22.5 & 17.5 & 20.5 & 19 \\
   & 21 & 13.5 & 19.5 & 19 & 26 & 18 & 23 & 17 \\
   & 21 & 18 & 21 & 17 & 20.5 & 16 & 22.5 & 15.5 \\
\hline
{\it Papilio} &  20 & 11.5 & 21.5 & 11 & 18.5 & 10 & 20 & 11 \\
{\it rutulus} &  19 & 11 & 20.5 & 11 & 19.5 & 11 & 19 & 10.5 \\
              &  21.5 & 11 & 20 & 11.5 & 21.5 & 10 & 20.5 & 12 \\
              &  20 & 10.5 & 21.5 & 12.5 & 17.5 & 12 & 21 & 12.5\\
              & 21 & 11.5 & 21 & 12 & 19 & 10.5 & 19 & 11\\
              & 18 & 11.5 & 21.5 & 10.5 & 23 & 11 & 22.5 & 11.5\\
              & 19 & 13 & 22.5 & 14 & 21 & 12.5 & 19.5 & 12.5\\
\hline
\end{tabular}
\end{table}

\newpage

\begin{figure}[h]
\begin{center}~
{\psfig{figure=fi1.eps,width=3in,height=3in}}
\end{center}
\label{fig:1}
\caption{Bayes rule}
\end{figure}

\end{document}
