Statistical Challenges In Modern Astronomy Ii
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Statistical Challenges in Modern Astronomy II
Author: G. Jogesh Babu
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Modern astronomical research faces a vast range of statistical issues which have spawned a revival in methodological activity among astronomers. The Statistical Challenges in Modern Astronomy II conference, held in June 1996 at the Pennsylvania State University five years after the first conference, brought astronomers and statisticians together to discuss methodological issues of common interest. Time series analysis, image analysis, Bayesian methods, Poisson processes, nonlinear regression, maximum likelihood, multivariate classification, and wavelet and multiscale analyses were important themes. Astronomers frequently encounter troublesome situations such as heteroscedastic weighting of data, unevenly spaced time series, and selection effects leading to censoring and truncation. Many problems were introduced at the conference in the context of large-scale astronomical projects inlcuding LIGO, AXAF, XTE, Hipparcos, and digitized sky surveys.This volume will be of interest to researchers and advanced students in both fields-astronomers who seek exposure to recent developments in statistics, and statisticians interested in confronting new problems. It is edited by two faculty members of the Pennsylvania State University who have a long-standing cross-disciplinary collaboration and jointly authored the recent introductory monograph "Astrostatics." G.J. Babu is Professor of Statistics, Fellow of the Institute of Mathematical Statistics, and Associate Editor of the Journal of Statistical Planning & Inference and the Journal of Nonparametric Statistics. Eric D. Feigelson is Professor of Astronomoy and Astrophysics.
Statistical Challenges in Modern Astronomy
Author: Eric D. Feigelson
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Modern astronomy has been characterized by an enormous growth in data acquisition - from new technologies in telescopes, detectors, and computation. One can now compile catalogs of tens or hundreds of millions of stars or galaxies and databases from satellite-based observations are reaching terabit proportions. This wealth of data gives rise to statistical challenges not previously encountered in astronomy. This book is the result of a workshop held at Pennsylvania State University in August 1991 that brought together leading astronomers and statisticians to consider statistical challenges encountered in modern astronomical research. The chapters have all been thoroughly revised in the light of the discussions at the conference, and some of the lively discussion is recorded here as well.
Statistical Challenges in Astronomy
Author: Eric D. Feigelson
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-05-26
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.