Researchers in observational astronomy often encounter situations where the scientific goals require a statistical interpretation of rather complex data. These problems cover a vast range of statistical issues, such as:
Feigelson and Babu organized international conferences called Statistical Challenges in Modern Astronomy (SCMA) held at Penn State. The purpose is to enhance the dialog between astronomers and statisticians on important research issues. Proceedings of the first (1991), second (1996) and third (2001) SCMA conferences are available. In 1996, we wrioe a brief monograph called Astrostatistics which introduces astronomer and statistician readers to the other field, and provides the first overview of astrostatistical issues. An extensive bibliography and index assists readers in pursuing topics introduced in the book. Drs. Feigelson and Babu have spoken at many meetings in both fields, promoting improvments in statistical methodology for observational astronomy, with an emphasis on time series analysis and multivariate analysis. In the latter context, we published a multivariate study of gamma-ray bursts which reports the discovery of a new class of GRBs.
During the late 1990s, Dr. Feigelson joined with Drs. Akritas in Statistics to form the Statistical Consulting Center for Astronomy. Operating by email and WWW, it gave quick reliable statistical advice for practicing astronomers as they encounter statistical problems. A wide range of problems are addressed such as appropriate treatment of known measurement errors, interpretation of Poisson processes, and parameter estimation in nonlinear regression.
We also created a popular Web metasite called
which provides hypertext links to many statistical codes and services on
the Web. Among many codes available from StatCodes are three developed at
Penn State: ASURV
implementing a suite of survival methods treating censored data (i.e.
providing several weighted and unweighted linear regressions with analytical
and bootstrap error analysis, and
for estimating correlation in multiply-censored multivariate datasets.