Astrostatistical Challenges For The New Astronomy
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| Author | : Joseph M. Hilbe |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 247 |
| Release | : 2012-11-07 |
| Genre | : Mathematics |
| ISBN | : 1461435080 |
Astrostatistical Challenges for the New Astronomy presents a collection of monographs authored by several of the disciplines leading astrostatisticians, i.e. by researchers from the fields of statistics and astronomy-astrophysics, who work in the statistical analysis of astronomical and cosmological data. Eight of the ten monographs are enhancements of presentations given by the authors as invited or special topics in astrostatistics papers at the ISI World Statistics Congress (2011, Dublin, Ireland). The opening chapter, by the editor, was adapted from an invited seminar given at Los Alamos National Laboratory (2011) on the history and current state of the discipline; the second chapter by Thomas Loredo was adapted from his invited presentation at the Statistical Challenges in Modern Astronomy V conference (2011, Pennsylvania State University), presenting insights regarding frequentist and Bayesian methods of estimation in astrostatistical analysis. The remaining monographs are research papers discussing various topics in astrostatistics. The monographs provide the reader with an excellent overview of the current state astrostatistical research, and offer guidelines as to subjects of future research. Lead authors for each chapter respectively include Joseph M. Hilbe (Jet Propulsion Laboratory and Arizona State Univ); Thomas J. Loredo (Dept of Astronomy, Cornell Univ); Stefano Andreon (INAF-Osservatorio Astronomico di Brera, Italy); Martin Kunz ( Institute for Theoretical Physics, Univ of Geneva, Switz); Benjamin Wandel ( Institut d'Astrophysique de Paris, Univ Pierre et Marie Curie, France); Roberto Trotta (Astrophysics Group, Dept of Physics, Imperial College London, UK); Phillip Gregory (Dept of Astronomy, Univ of British Columbia, Canada); Marc Henrion (Dept of Mathematics, Imperial College, London, UK); Asis Kumar Chattopadhyay (Dept of Statistics, Univ of Calcutta, India); Marisa March (Astrophysics Group, Dept of Physics, Imperial College, London, UK)./body
| Author | : Eric D. Feigelson |
| Publisher | : Cambridge University Press |
| Total Pages | : 495 |
| Release | : 2012-07-12 |
| Genre | : Science |
| ISBN | : 052176727X |
Modern Statistical Methods for Astronomy: With R Applications.
| Author | : Joseph M. Hilbe |
| Publisher | : CRC Press |
| Total Pages | : 170 |
| Release | : 2016-04-05 |
| Genre | : Mathematics |
| ISBN | : 1498709583 |
Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fishe
| Author | : George H. Rieke |
| Publisher | : Cambridge University Press |
| Total Pages | : 394 |
| Release | : 2017-05-25 |
| Genre | : Science |
| ISBN | : 1139536079 |
Astronomy is an observational science, renewed and even revolutionized by new developments in instrumentation. With the resulting growth of multiwavelength investigation as an engine of discovery, it is increasingly important for astronomers to understand the underlying physical principles and operational characteristics for a broad range of instruments. This comprehensive text is ideal for graduate students, active researchers and instrument developers. It is a thorough review of how astronomers obtain their data, covering current approaches to astronomical measurements from radio to gamma rays. The focus is on current technology rather than the history of the field, allowing each topic to be discussed in depth. Areas covered include telescopes, detectors, photometry, spectroscopy, adaptive optics and high-contrast imaging, millimeter-wave and radio receivers, radio and optical/infrared interferometry, and X-ray and gamma-ray astronomy, all at a level that bridges the gap between the basic principles of optics and the subject's abundant specialist literature. Color versions of figures and solutions to selected problems are available online at www.cambridge.org/9780521762298.
| Author | : Joseph M. Hilbe |
| Publisher | : Cambridge University Press |
| Total Pages | : 301 |
| Release | : 2014-07-21 |
| Genre | : Business & Economics |
| ISBN | : 1107028337 |
This book provides guidelines and fully worked examples of how to select, construct, interpret and evaluate the full range of count models.
| Author | : Kieran Jay Edwards |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 112 |
| Release | : 2014-04-12 |
| Genre | : Technology & Engineering |
| ISBN | : 3319065998 |
With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as “Uncertain”. This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.
| Author | : Gutti Jogesh Babu |
| Publisher | : CRC Press |
| Total Pages | : 242 |
| Release | : 1996-08-01 |
| Genre | : Mathematics |
| ISBN | : 9780412983917 |
Modern astronomers encounter a vast range of challenging statistical problems, yet few are familiar with the wealth of techniques developed by statisticians. Conversely, few statisticians deal with the compelling problems confronted in astronomy. Astrostatistics bridges this gap. Authored by a statistician-astronomer team, it provides professionals and advanced students in both fields with exposure to issues of mutual interest. In the first half of the book the authors introduce statisticians to stellar, galactic, and cosmological astronomy and discuss the complex character of astronomical data. For astronomers, they introduce the statistical principles of nonparametrics, multivariate analysis, time series analysis, density estimation, and resampling methods. The second half of the book is organized by statistical topic. Each chapter contains examples of problems encountered astronomical research and highlights methodological issues. The final chapter explores some controversial issues in astronomy that have a strong statistical component. The authors provide an extensive bibliography and references to software for implementing statistical methods. The "marriage" of astronomy and statistics is a natural one and benefits both disciplines. Astronomers need the tools and methods of statistics to interpret the vast amount of data they generate, and the issues related to astronomical data pose intriguing challenges for statisticians. Astrostatistics paves the way to improved statistical analysis of astronomical data and provides a common ground for future collaboration between the two fields.
| Author | : Eric D. Feigelson |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 544 |
| Release | : 2012-08-15 |
| Genre | : Mathematics |
| ISBN | : 146143520X |
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.
| Author | : Justine Shults |
| Publisher | : CRC Press |
| Total Pages | : 223 |
| Release | : 2014-01-28 |
| Genre | : Mathematics |
| ISBN | : 1420099930 |
Drawing on the authors’ substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression—a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitudinal data, familial data, and data with multiple sources of correlation. In some settings, QLS also allows for improved analysis with an unstructured correlation matrix. Special focus is given to goodness-of-fit analysis as well as new strategies for selecting the appropriate working correlation structure for QLS and GEE. A chapter on longitudinal binary data tackles recent issues raised in the statistical literature regarding the appropriateness of semi-parametric methods, such as GEE and QLS, for the analysis of binary data; this chapter includes a comparison with the first-order Markov maximum-likelihood (MARK1ML) approach for binary data. Examples throughout the book demonstrate each topic of discussion. In particular, a fully worked out example leads readers from model building and interpretation to the planning stages for a future study (including sample size calculations). The code provided enables readers to replicate many of the examples in Stata, often with corresponding R, SAS, or MATLAB® code offered in the text or on the book’s website.
| Author | : Luis Manuel Sarro |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 259 |
| Release | : 2012-08-04 |
| Genre | : Science |
| ISBN | : 1461433231 |
This volume provides an overview of the field of Astrostatistics understood as the sub-discipline dedicated to the statistical analysis of astronomical data. It presents examples of the application of the various methodologies now available to current open issues in astronomical research. The technical aspects related to the scientific analysis of the upcoming petabyte-scale databases are emphasized given the importance that scalable Knowledge Discovery techniques will have for the full exploitation of these databases. Based on the 2011 Astrostatistics and Data Mining in Large Astronomical Databases conference and school, this volume gathers examples of the work by leading authors in the areas of Astrophysics and Statistics, including a significant contribution from the various teams that prepared for the processing and analysis of the Gaia data.
