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Statistical Methodology for Large Astronomical Surveys

Published online by Cambridge University Press:  25 May 2016

E.D. Feigelson and
G.J. Babu
E.D. Feigelson
Affiliation:
Dept. of Astron & Astrophys, Pennsylvania State University
G.J. Babu
Affiliation:
Dept. of Statistics, Pennsylvania State University

Abstract

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Multiwavelength surveys present a variety of challenging statistical problems: raw data processing, source identification, source characterization and classification, and interrelations between multiwavelength properties. For these last two issues, we discuss the applicability of standard and new multivariate statistical techniques. Traditional methods such as ANOVA, principal components analysis, cluster analysis, and tests for multivariate linear hypotheses are underutilized in astronomy and can be very helpful. Newer statistical methods such as projection pursuit, multivariate splines, and visualization tools such as XGobi are briefly introduced. However, multivariate databases from astronomical surveys present significant challenges to the statistical community. These include treatments of heteroscedastic measurement errors, censoring and truncation due to flux limits, and parameter estimation for nonlinear astrophysical models.

Information

Type
Part 7. Data Processing Techniques
Information
Symposium - International Astronomical Union , Volume 179: New Horizons from Multi-Wavelength Sky Surveys , 1998, pp. 363 - 370
Copyright
Copyright © Kluwer 1998 

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