Statistical challenges in modern astronomy V [electronic resource] / Eric D. Feigelson, G. Jogesh Babu, editors.
Now beginning its third decade, the Statistical Challenges in Modern Astronomy (SCMA) conferences are the premier forums where astronomers and statisticians discuss advanced methodological issues arising in astronomical research. ¡From cosmology to exoplanets, astronomers produce enormous datasets a...
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| Other title: | Statistical challenges in modern astronomy 5. |
| Format: | Electronic Conference Proceeding eBook |
| Language: | English |
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New York, NY :
Springer,
©2012.
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| Series: | Lecture notes in statistics (Springer-Verlag). Proceedings ;
209. |
| Subjects: |
MARC
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| 111 | 2 | |a Statistical Challenges in Modern Astronomy |n (5th : |d 2011 : |c Penn State University) | |
| 245 | 1 | 0 | |a Statistical challenges in modern astronomy V |h [electronic resource] / |c Eric D. Feigelson, G. Jogesh Babu, editors. |
| 246 | 3 | |a Statistical challenges in modern astronomy 5. | |
| 264 | 1 | |a New York, NY : |b Springer, |c ©2012. | |
| 300 | |a 1 online resource : |b color illustrations. | ||
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| 490 | 1 | |a Lecture notes in statistics, Proceedings, |x 0930-0325 ; |v 209. | |
| 500 | |a International conference proceedings. | ||
| 505 | 0 | 0 | |g Part 1. |t Statistics in Cosmology -- |t Likelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions / |r Chad M. Schafer and Peter E. Freeman -- |t Commentary: Likelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions / |r Martin A. Hendry -- |t Robust, Data-Driven Inference in Non-linear Cosmostatistics / |r Benjamin D. Wandelt, Jens Jasche and Guilhem Lavaux -- |t Simulation-Aided Inference in Cosmology / |r David Higdon, Earl Lawrence, Katrin Heitmann and Salman Habib -- |t Commentary: Simulation-Aided Inference in Cosmology / |r Carlo Graziani -- |t The Matter Spectral Density from Lensed Cosmic Microwave Background Observations / |r Ethan Anderes and Alexander van Engelen -- |t Commentary: 'The Matter Spectral Density from Lensed Cosmic Microwave Background Observations' / |r Alan Heavens -- |t Needlets Estimation in Cosmology and Astrophysics / |r Domenico Marinucci. |
| 505 | 8 | 0 | |g Part 2. |t Bayesian Analysis Across Astronomy -- |t Parameter Estimation and Model Selection in Extragalactic Astronomy / |r Martin D. Weinberg -- |t Commentary: Bayesian Model Selection and Parameter Estimation / |r Philip C. Gregory -- |t Cosmological Bayesian Model Selection: Recent Advances and Open Challenges / |r Roberto Trotta -- |t Commentary: Cosmological Bayesian Model Selection / |r David A. van Dyk -- |t Measurement Error Models in Astronomy / |r Brandon C. Kelly -- |t Commentary: "Measurement Error Models in Astronomy" by Brandon C. Kelly / |r David Ruppert -- |t Asteroseismology: Bayesian Analysis of Solar-Like Oscillators / |r Othman Benomar -- |t Semi-parametric Robust Event Detection for Massive Time-Domain Databases / |r Alexander W. Blocker and Pavlos Protopapas -- |t Bayesian Analysis of Reverberation Mapping Data / |r Brendon J. Brewer -- |t Bayesian Mixture Models for Poisson Astronomical Images / |r Fabrizia Guglielmetti, Rainer Fischer and Volker Dose -- |t Systematic Errors in High-Energy Astrophysics / |r Vinay Kashyap -- |t Hierarchical Bayesian Models for Type Ia Supernova Inference / |r Kaisey S. Mandel -- |t Bayesian Flux Reconstruction in One and Two Bands / |r Eric R. Switzer, Thomas M. Crawford and Christian L. Reichardt -- |t Commentary: Bayesian Analysis Across Astronomy / |r Thomas J. Loredo. |
| 505 | 8 | 0 | |g Part 3. |t Data Mining and Astroinformatics -- |t Sparse Astronomical Data Analysis / |r Jean-Luc Starck -- |t Exploiting Non-linear Structure in Astronomical Data for Improved Statistical Inference / |r Ann B. Lee and Peter E. Freeman -- |t Commentary: Exploiting Non-linear Structure in Astronomical Data for Improved Statistical Inference / |r Didier Fraix-Burnet -- |t Surprise Detection in Multivariate Astronomical Data / |r Kirk D. Borne and Arun Vedachalam -- |t On Statistical Cross-Identification in Astronomy / |r Tamás Budavári -- |t Commentary: On Statistical Cross-Identification in Astronomy / |r Thomas J. Loredo -- |t Data Compression Methods in Astrophysics / |r Raul Jimenez -- |t Commentary: Data Compression Methods in Astrophysics / |r Ann B. Lee. |
| 505 | 8 | 0 | |g Part 4. |t Image and Time Series Analysis -- |t Morphological Image Analysis and Sunspot Classification / |r David Stenning, Vinay Kashyap, Thomas C.M. Lee, David A. van Dyk and C. Alex Young -- |t Commentary: Morphological Image Analysis and Sunspot Classification / |r Ricardo Vilalta -- |t Learning About the Sky Through Simulations / |r Andrew Connolly, John Peterson, Garret Jernigan, D. Bard and the LSST Image Simulation Group -- |t Commentary: Learning About the Sky Through Simulations / |r Michael J. Way -- |t Statistical Analyses of Data Cubes / |r Erik Rosolowsky -- |t Astronomical Transient Detection Controlling the False Discovery Rate / |r Nicolle Clements, Sanat K. Sarkar and Wenge Guo -- |t Commentary: Astronomical Transient Detection Controlling the False Discovery Rate / |r Peter E. Freeman -- |t Slepian Wavelet Variances for Regularly and Irregularly Sampled Time Series / |r Debashis Mondal and Donald B. Percival -- |t Commentary: Slepian Wavelet Variances for Regularly and Irregularly Samples Time Series / |r Jeffrey D. Scargle. |
| 505 | 8 | 0 | |g Part 5. |t The Future of Astrostatistics -- |t Astrostatistics in the International Arena / |r Joseph M. Hilbe -- |t The R Statistical Computing Environment / |r Luke Tierney -- |t Panel Discussion: The Future of Astrostatistics / |r G. Jogesh Babu. |
| 505 | 8 | 0 | |g Part 6. |t Contributed Papers -- |t Bayesian Estimation of log N -- log S / |r Paul D. Baines, Irina S. Udaltsova, Andreas Zezas and Vinay L. Kashyap -- |t Techniques for Massive-Data Machine Learning in Astronomy / |r Nicholas M. Ball -- |t A Bayesian Approach to Gravitational Lens Model Selection / |r Irene Balmès -- |t Identification of Outliers Through Clustering and Semi-supervised Learning for All Sky Surveys / |r Sharmodeep Bhattacharyya, Joseph W. Richards, John Rice, Dan L. Starr and Nathaniel R. Butler, et al. -- |t Estimation of Moments on the Sphere by Means of Fast Convolution / |r P. Bielewicz, B.D. Wandelt and A.J. Banday -- |t Variability Detection by Change-Point Analysis / |r Seo-Won Chang, Yong-Ik Byun and Jaegyoon Hahm -- |t Evolution as a Confounding Parameter in Scaling Relations for Galaxies / |r Didier Fraix-Burnet -- |t Detecting Galaxy Mergers at High Redshift / |r P.E. Freeman, R. Izbicki, Ann B. Lee, C. Schafer and D. Slepčev, et al. |
| 505 | 8 | 0 | |t Multi-component Analysis of a Sample of Bright X-Ray Selected Active Galactic Nuclei / |r Dirk Grupe -- |t Applying the Background-Source Separation Algorithm to Chandra Deep Field South Data / |r F. Guglielmetti, H. Böhringer, R. Fischer, P. Rosati and P. Tozzi -- |t Non-Gaussian Physics of the Cosmological Genus Statistic / |r J. Berian James -- |t Modeling Undetectable Flares / |r Vinay Kashyap, Steve Saar, Jeremy Drake, Kathy Reeves and Jennifer Posson-Brown, et al. -- |t An F-Statistic Based Multi-detector Veto for Detector Artifacts in Gravitational Wave Data / |r D. Keitel, R. Prix, M.A. Papa and M. Siddiqi -- |t Constrained Probability Distributions of Correlation Functions / |r D. Keitel and P. Schneider -- |t Improving Weak Lensing Reconstructions in 3D Using Sparsity / |r Adrienne Leonard, François-Xavier Dupé and Jean-Luc Starck -- |t Bayesian Predictions from the Semi-analytic Models of Galaxy Formation / |r Yu Lu, H.J. Mo, Martin D. Weinberg and Neal Katz -- |t Statistical Issues in Galaxy Cluster Cosmology / |r Adam Mantz, Steven W. Allen and David Rapetti. |
| 505 | 8 | 0 | |t Statistical Analyses to Understand the Relationship Between the Properties of Exoplanets and Their Host Stars / |r Elizabeth Martínez-Gómez -- |t Identifying High-z Gamma-Ray Burst Candidates Using Random Forest Classification / |r Adam N. Morgan, James Long, Tamara Broderick, Joseph W. Richards and Joshua S. Bloom -- |t Fitting Distributions of Points Using [tau]2 / |r Tim Naylor -- |t Theoretical Power Spectrum Estimation from Cosmic Microwave Background Data / |r Paniez Paykari, Jean-Luc Starck and M. Jalal Fadili -- |t Guilt by Association: Finding Cosmic Ray Sources Using Hierarchical Bayesian Clustering / |r Kunlaya Soiaporn, David Chernoff, Thomas Loredo, David Ruppert and Ira Wasserman -- |t Statistical Differences Between Swift Gamma-Ray Burst Classes Based on [gamma]- and X-ray Observations / |r Dorottya Szécsi, Lajos G. Balázs, Zsolt Bagoly, István Horváth and Attila Mészáros, et al. -- |t A Quasi-Gaussian Approximation for the Probability Distribution of Correlation Functions / |r Philipp Wilking and Peter Schneider -- |t New Insights into Galaxy Structure from GALPHAT / |r Ilsang Yoon, Martin Weinberg and Neal Katz. |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a Now beginning its third decade, the Statistical Challenges in Modern Astronomy (SCMA) conferences are the premier forums where astronomers and statisticians discuss advanced methodological issues arising in astronomical research. ¡From cosmology to exoplanets, astronomers produce enormous datasets and encounter difficult modeling issues to arrive at astrophysical insights. ¡At the SCMA V conference held at Penn State University in June 2011, researchers from around the world presented the latest astrostatistical methods. ¡To promote cross-disciplinary perspectives, each lecture from an expert in one field is followed by a commentary from the other field. A wide range of statistical developments are highlighted in the SCMA V conference. ¡Some focus on problems arising in precision cosmology involving characteristics of the cosmic microwave background, galaxy clustering and gravitational lensing. ¡Bayesian approaches are particularly important in this and other areas. ¡Knowledge discovery from megadatasets brings methods of data mining into use. Image analysis and time series analysis are areas where astronomers perennially wrestle with sophisticated modeling problems. ¡The proceedings ends with discussion of the future of astrostatistics.¡ Eric D. Feigelson, Professor of Astronomy & Astrophysics, and G. Jogesh Babu, Professor of Statistics, have long collaborated in cross-disciplinary research and services. ¡Under the auspices of Penn State's Center for Astrostatistics, they run the SCMA conferences, offer summer schools in statistics for astronomers, produce texts and research articles promoting advances in statistical methodology in astronomy. ¡Feigelson also conducts research in X-ray astronomy and star formation, and Babu is a mathematical statistician with interest in bootstrap methods, nonparametrics and asymptotic theory. | ||
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