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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 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.
Astronomy has become data-driven in ways that are both
quantitatively and qualitatively different from the past: data
structures are not simple; procedures to gain astrophysical
insights are not obvious; and the informational content of the data
sets is so high that archival research and data mining are not
merely convenient, but obligatory, as researchers who obtain the
data can only extract a small fraction of the science enabled by
it. IAU Symposium 325 took place at a crucial stage in the
development of the field, when many efforts have carried
significant achievements, but the widespread groups have just begun
to effectively communicate across specialties, to gather and
assimilate their achievements, and to consult cross-disciplinary
experts. Bringing together astronomers involved in surveys and
large simulation projects, computer scientists, data scientists,
and companies, this volume showcases their fruitful exchange of
ideas, methods, software, and technical capabilities.
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.
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