Statistical Challenges in Astronomy

  • Eric D. Feigelson
  • G. Jogesh Babu

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Thomas J. Loredo, David F. Chernoff
    Pages 57-70
  3. James O. Berger, William H. Jefferys, Peter Muller, Thomas G. Barnes
    Pages 71-88
  4. Joseph H. Bredekamp, Daniel A. Golombek
    Pages 103-112
  5. S. G. Djorgovski, R. Brunner, A. Mahabal, R. Williams, R. Granat, P. Stolorz
    Pages 127-141
  6. Vicent J. Martínez, Enn Saar
    Pages 143-160
  7. Alexander S. Szalay, Takahiko Matsubara
    Pages 161-174
  8. Andrew H. Jaffe
    Pages 197-214
  9. Chad M. Schafer, Philip B. Stark
    Pages 215-219
  10. The Pittsburgh Institute for Computational Astrostatistics (PICA)
    Pages 221-241
  11. Leo Breiman, Michael Last, John Rice
    Pages 243-254
  12. Robert C. Nichol, S. Chong, A. J. Connolly, S. Davies, C. Genovese, A. M. Hopkins et al.
    Pages 265-278
  13. Fionn D. Murtagh
    Pages 279-292

About these proceedings


Digital sky surveys, high-precision astrometry from satellite data, deep-space data from orbiting telescopes, and the like have all increased the quantity and quality of astronomical data by orders of magnitude per year for several years. Making sense of this wealth of data requires sophisticated statistical techniques. Fortunately, statistical methodologies have similarly made great strides in recent years. Powerful synergies thus emerge when astronomers and statisticians join in examining astrostatistical problems and approaches.

The book begins with an historical overview and tutorial articles on basic cosmology for statisticians and the principles of Bayesian analysis for astronomers. As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Thus, for example, an overview of Bayesian methods for Poissonian data is joined by discussions of planning astronomical observations with optimal efficiency and nested models to deal with instrumental effects.

The principal theme for the volume is the statistical methods needed to model fundamental characteristics of the early universe on its largest scales.


Astrometry Astronomical Observation Cluster analysis Cosmology Galaxy Star Time series Universe astronomy clustering data analysis quasar

Authors and affiliations

  • Eric D. Feigelson
    • 1
  • G. Jogesh Babu
    • 2
  1. 1.Department of Astronomy and AstrophysicsPennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of StatisticsPennsylvania State UniversityUniversity ParkUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York, Inc. 2003
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-95546-9
  • Online ISBN 978-0-387-21529-7
  • About this book