Improved Astronomical Inferences via Nonparametric Density Estimation

Details Improved Astronomical Inferences via Nonparametric Density Estimation Improved Astronomical Inferences via Nonparametric Density Estimation

Jan 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21523106s&link_type=abstract

American Astronomical Society, AAS Meeting #215, #231.06; Bulletin of the American Astronomical Society, Vol. 42, p.595

Astronomy and Astrophysics

Astronomy

Scientific paper

Nonparametric and semiparametric approaches to density estimation can yield scientific insights unavailable when restrictive assumptions are made regarding the form of the distribution. Further, when a well-chosen dimension reduction technique is utilized, the distribution of high-dimensional data (e.g., spectra, images) can be characterized via a nonparametric approach. The hope is that these procedures will preserve a large amount of the rich information in these data. Ideas will be illustrated via a semiparametric approach to estimating luminosity functions (Schafer, 2007) and recent work on characterizing the evolution of the distribution of galaxy morphology.
This is joint work with Peter Freeman, Susan Buchman, and Ann Lee. Work is supported by NASA AISR Grant.

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