\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}
\begin{document}

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

\begin{center}
  4:15 p.m., Tuesday, March 7, 2006\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
\end{center}

\begin{center}
  \textsl{Armin Schwartzman }\\
 Department of Statistics \\
Stanford University\\
\end{center}

\begin{center}
  \textbf{Random Ellipsoids and False Discovery Rates: \\
Statistics for Diffusion Tensor Imaging Data}
\end{center}


\noindent
Diffusion Tensor Imaging (DTI) is a new form of Magnetic Resonance Imaging
(MRI) that is revolutionizing brain research as it allows insight into the
structure of the white matter. As opposed to standard MRI, DTI measurements
at each volume element are not scalars but three-dimensional ellipsoids,
represented mathematically as 3-by-3 positive definite matrices. This is a
new form of data for which standard statistical methods do not apply. In
this work, I propose a new probability model for random ellipsoids based on
normal random matrix theory, and derive estimation and testing tools for the
ellipsoidsÕ eigenvalues and eigenvectors. In addition, by framing the
problem of comparing images as a multiple comparisons problem, interesting
spatial locations are found using false discovery rate inference combined
with a new form of the empirical null for one-sided tests. These methods
are shown in the context of a DTI study of reading ability in children.


\end{document}