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 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 all important themes. Many problems were introduced at the conference in the context of large-scale astronomical projects including LIGO, AXAF, XTE, Hipparcos, and digitised sky surveys. As such, this volume will be of interest to researchers and advanced students in both fields - astronomers seeking exposure to recent developments in statistics, and statisticians interested in confronting new problems.

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.

Digital sky surveys, data from orbiting telescopes, and advances in computation have increased the quantity and quality of astronomical data by several orders of magnitude in recent years. Making sense of this wealth of data requires sophisticated statistical and data analytic 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 volume focuses on several themes: .The increasing power of Bayesian approaches to modeling astronomical data .The growth of enormous databases, leading an emerging federated Virtual Observatory, and their impact on modern astronomical research .Statistical modeling of critical datasets, such as galaxy clustering and fluctuations in the microwave background radiation, leading to a new era of precision cosmology .Methodologies for uncovering clusters and patterns in multivariate data .The characterization of multiscale patterns in imaging and time series data As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Short contributed papers covering dozens of astrostatistical topics are also included."

Digital sky surveys, data from orbiting telescopes, and advances in computation have increased the quantity and quality of astronomical data by several orders of magnitude in recent years. Making sense of this wealth of data requires sophisticated statistical and data analytic 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 volume focuses on several themes: · The increasing power of Bayesian approaches to modeling astronomical data · The growth of enormous databases, leading an emerging federated Virtual Observatory, and their impact on modern astronomical research · Statistical modeling of critical datasets, such as galaxy clustering and fluctuations in the microwave background radiation, leading to a new era of precision cosmology · Methodologies for uncovering clusters and patterns in multivariate data · The characterization of multiscale patterns in imaging and time series data As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Short contributed papers covering dozens of astrostatistical topics are also included.

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.

This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research. It draws from three main themes throughout: the finite-sample theory, the asymptotic theory, and Bayesian statistics. The authors have included a chapter on estimating equations as a means to unify a range of useful methodologies, including generalized linear models, generalized estimation equations, quasi-likelihood estimation, and conditional inference. They also utilize a standardized set of assumptions and tools throughout, imposing regular conditions and resulting in a more coherent and cohesive volume. Written for the graduate-level audience, this text can be used in a one-semester or two-semester course.

This volume contains a selection of chapters based on papers to be presented at the Fifth Statistical Challenges in Modern Astronomy Symposium. The symposium will be held June 13-15th at Penn State University. 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 V conference will bring 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 are all important themes to be covered in detail. Many problems will be introduced at the conference in the context of large-scale astronomical projects including LIGO, AXAF, XTE, Hipparcos, and digitized sky surveys.

Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of nondetections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it invaluable for graduate students and researchers facing complex data analysis tasks. A link to the author's website for this book can be found at www.cambridge.org/msma. Material available on their website includes datasets, R code and errata. Visit the author's homepage at http: //astrostatistics.psu.edu for more materials.