**AAS 196th Meeting, June 2000**

*Session 60. New Statistics for New Missions: Problems and Opportunities for Breakthrough Thinking*

Special Session Oral, Thursday, June 8, 2000, 2:00-3:30pm, Highland A/K
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## [60.01] Nonparametric Statistical Methods in Astrophysics

*L. Wasserman (Carnegie Mellon)*

Nonparametric statistical methods allow one to analyze data
without making strong assumptions about the process that
generated the data. For example, instead of assuming that
the data have a Gaussian distribution, we might assume only
that the distribution has a probability density that
satisfies some weak, smoothness conditions. I will discuss
three methods for estimating probability density functions:
mixture models, kernel density estimation and wavelets.
Finally, I will illustrate these methods applied to
Astrophysics data. These applications are based on a
collaboration between Astrophysicists (Andy Connolly, Bob
Nichol), Computer Scientists (Andrew Moore, Jeff Schneider)
and Statisticians (Chris Genovese and me).

The author(s) of this abstract have provided an email address
for comments about the abstract:
larry@stat.cmu.edu

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