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  \textbf{\textsc{STANFORD UNIVERSITY}}\\[5pt]
  \textbf{\textsc{DEPARTMENT OF STATISTICS}}\\[5pt]
  \Large{\textbf\textsc{{DEPARTMENTAL SEMINAR}}}
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\begin{center}
  4:15 p.m., Tuesday, May 31, 2005\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Rick Paik Schoenberg}\\
  Department of Statistics\\
  UCLA\\
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  \textbf{Separable Point Processes and their use in Wildfire Risk Estimation}
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Wildfires pose an extremely serious threat to California each year, 
damaging vast areas of public and private property and often 
threatening human lives. Wildfire risk assessments are presently made 
primarily using the Burning Index (BI), a numerical rating issued by 
the USDA Department of Forestry. Unfortunately, the BI appears to be 
a very poor predictor of wildfires. Because so many variables are 
positively associated with wildfire risk, the construction of models 
to compete with the BI is quite a difficult task. The problem is 
essentially the familiar "curse of dimensionality," and is greatly 
alleviated when different components in the process may be estimated 
separately. Some results will be presented concerning situations 
where such separability is permitted, and their use in predicting 
wildfires in Los Angeles County will be explored.
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