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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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   4:15 p.m., Tuesday, November 27, 2007\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Regina Liu} \\
  Department of Statistics\\
  Rutgers University 
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  \subsection*{Mining Massive Text Data: Classification, Construction of Tracking Statistics and Inference under Misclassification}
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\noindent
We present a systematic data mining procedure for exploring
large free-style text datasets to discover useful features and
develop tracking statistics (often referred to as performance
measures or risk indicators). The procedure includes text
classification, construction of tracking statistics, inference
under error measurements and risk management. The main
difficulty in deriving this inference scheme is the accounting
for misclassification errors, for which we propose two types
of approaches: "plug-in" and "projection" methods. We
also consider the bootstrap calibration for fine tuning.
Finally, as an illustrative example, the proposed data mining 
procedure is applied to analyzing an FAA aviation safety 
report repository to show its utility in aviation risk 
management or general decision-support systems.

Although most illustrations here are drawn from aviation
safety data, the proposed data mining procedure applies to
many other domains, including, for example, mining free-style
medical reports for tracking medical errors or possible disease 
outbreaks.

This is joint work with Daniel Jeske, Department of Statistics,
UC Riverside.

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