\documentclass[11pt]{article}
\setlength{\oddsidemargin}{0.0truein}
\setlength{\evensidemargin}{0.0truein}
\setlength{\textwidth}{6.5truein}
\setlength{\topmargin}{0.0truein}
\setlength{\textheight}{9.0truein} \setlength{\headsep}{0.0truein}
\setlength{\headheight}{0.0truein} \setlength{\topskip}{10.0pt}
\setlength{\parskip}{5mm}
\usepackage{url}
\usepackage{amsmath}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}

\begin{center}
  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
\end{center}

% In the following statements, replace "Time of talk", 
% "Weekday", and "Date of talk". An example is provided. 
% If you are not sure about this, just skip this part.


\begin{center}
  4:15 p.m., Tuesday, January 22, 2008\\
  %% Example: 4:15 p.m., Tuesday, February 13, 2007\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
\end{center}

% In the following statements, replace "Name of the speaker" with your
% name, "Department Affiliation" with your department affiliation, and
%"University Affiliation" with your university affiliation.

\begin{center}
  \textsl{Youngjo Lee} \\
 %Affiliation\\
\end{center}

% In the following statements, replace "Title of the talk"
% with your title of the talk.

\begin{center}
  \subsection*{DHGLMs for LASSO}
\end{center}

% In the following statements, replace "Abstract of the talk"
% with your abstract.

\noindent
GLMs have been extensively used in data analysis. The model classes have been extended to joint GLMs (Lee and Nelder, 1991), hierarchical GLMs (HGLMs, 1996) and double HGLMs (DHGLMs, 2006). For inference about the extended class of models, the Fisher likelihood has been extended to the h-likelihood, allowing unobservables such as random effects, missing data and unobserved future observations in the likelihood. In this talk I intent to put the LASSO type variable selection methods in the perspective of DHGLMs (and therefore under the extended likelihood framework) and show how the methods developed in extended clasees can enrich variable selections. 
\end{document}