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Statistical challenges in modern astronomy V /

Eric D. Feigelson, G. Jogesh Babu, editors.

Book Cover
Names: Feigelson, Eric D. | Babu, Gutti Jogesh,
Published: New York, NY : Springer, ©2012.
Series: Lecture notes in statistics (Springer-Verlag). Proceedings ; 209.
Topics: Statistical astronomy - Congresses. | Data mining - Congresses. | Astronomy. | Data Mining. | Statistics | SCIENCE - Astronomy.
Genres: Electronic books. | Congress. | Conference papers and proceedings.
Online Access: SpringerLink - Full text online
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111 2 |aStatistical Challenges in Modern Astronomy|n(5th :|d2011 :|cPenn State University)
245 10|aStatistical challenges in modern astronomy V /|cEric D. Feigelson, G. Jogesh Babu, editors.
246 3 |aStatistical challenges in modern astronomy 5
264 1|aNew York, NY :|bSpringer,|c©2012.
300 |a1 online resource :|bcolor illustrations.
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
347 |atext file|bPDF|2rda
490 1 |aLecture notes in statistics, Proceedings,|x0930-0325 ;|v209
500 |aInternational conference proceedings.
504 |aIncludes bibliographical references and index.
505 00|gPart 1.|tStatistics in Cosmology --|tLikelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions /|rChad M. Schafer and Peter E. Freeman --|tCommentary: Likelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions /|rMartin A. Hendry --|tRobust, Data-Driven Inference in Non-linear Cosmostatistics /|rBenjamin D. Wandelt, Jens Jasche and Guilhem Lavaux --|tSimulation-Aided Inference in Cosmology /|rDavid Higdon, Earl Lawrence, Katrin Heitmann and Salman Habib --|tCommentary: Simulation-Aided Inference in Cosmology /|rCarlo Graziani --|tThe Matter Spectral Density from Lensed Cosmic Microwave Background Observations /|rEthan Anderes and Alexander van Engelen --|tCommentary: 'The Matter Spectral Density from Lensed Cosmic Microwave Background Observations' /|rAlan Heavens --|tNeedlets Estimation in Cosmology and Astrophysics /|rDomenico Marinucci.
505 80|gPart 2.|tBayesian Analysis Across Astronomy --|tParameter Estimation and Model Selection in Extragalactic Astronomy /|rMartin D. Weinberg --|tCommentary: Bayesian Model Selection and Parameter Estimation /|rPhilip C. Gregory --|tCosmological Bayesian Model Selection: Recent Advances and Open Challenges /|rRoberto Trotta --|tCommentary: Cosmological Bayesian Model Selection /|rDavid A. van Dyk --|tMeasurement Error Models in Astronomy /|rBrandon C. Kelly --|tCommentary: "Measurement Error Models in Astronomy" by Brandon C. Kelly /|rDavid Ruppert --|tAsteroseismology: Bayesian Analysis of Solar-Like Oscillators /|rOthman Benomar --|tSemi-parametric Robust Event Detection for Massive Time-Domain Databases /|rAlexander W. Blocker and Pavlos Protopapas --|tBayesian Analysis of Reverberation Mapping Data /|rBrendon J. Brewer --|tBayesian Mixture Models for Poisson Astronomical Images /|rFabrizia Guglielmetti, Rainer Fischer and Volker Dose --|tSystematic Errors in High-Energy Astrophysics /|rVinay Kashyap --|tHierarchical Bayesian Models for Type Ia Supernova Inference /|rKaisey S. Mandel --|tBayesian Flux Reconstruction in One and Two Bands /|rEric R. Switzer, Thomas M. Crawford and Christian L. Reichardt --|tCommentary: Bayesian Analysis Across Astronomy /|rThomas J. Loredo.
505 80|gPart 3.|tData Mining and Astroinformatics --|tSparse Astronomical Data Analysis /|rJean-Luc Starck --|tExploiting Non-linear Structure in Astronomical Data for Improved Statistical Inference /|rAnn B. Lee and Peter E. Freeman --|tCommentary: Exploiting Non-linear Structure in Astronomical Data for Improved Statistical Inference /|rDidier Fraix-Burnet --|tSurprise Detection in Multivariate Astronomical Data /|rKirk D. Borne and Arun Vedachalam --|tOn Statistical Cross-Identification in Astronomy /|rTamás Budavári --|tCommentary: On Statistical Cross-Identification in Astronomy /|rThomas J. Loredo --|tData Compression Methods in Astrophysics /|rRaul Jimenez --|tCommentary: Data Compression Methods in Astrophysics /|rAnn B. Lee.
505 80|gPart 4.|tImage and Time Series Analysis --|tMorphological Image Analysis and Sunspot Classification /|rDavid Stenning, Vinay Kashyap, Thomas C.M. Lee, David A. van Dyk and C. Alex Young --|tCommentary: Morphological Image Analysis and Sunspot Classification /|rRicardo Vilalta --|tLearning About the Sky Through Simulations /|rAndrew Connolly, John Peterson, Garret Jernigan, D. Bard and the LSST Image Simulation Group --|tCommentary: Learning About the Sky Through Simulations /|rMichael J. Way --|tStatistical Analyses of Data Cubes /|rErik Rosolowsky --|tAstronomical Transient Detection Controlling the False Discovery Rate /|rNicolle Clements, Sanat K. Sarkar and Wenge Guo --|tCommentary: Astronomical Transient Detection Controlling the False Discovery Rate /|rPeter E. Freeman --|tSlepian Wavelet Variances for Regularly and Irregularly Sampled Time Series /|rDebashis Mondal and Donald B. Percival --|tCommentary: Slepian Wavelet Variances for Regularly and Irregularly Samples Time Series /|rJeffrey D. Scargle.
505 80|gPart 5.|tThe Future of Astrostatistics --|tAstrostatistics in the International Arena /|rJoseph M. Hilbe --|tThe R Statistical Computing Environment /|rLuke Tierney --|tPanel Discussion: The Future of Astrostatistics /|rG. Jogesh Babu.
505 80|gPart 6.|tContributed Papers --|tBayesian Estimation of log N -- log S /|rPaul D. Baines, Irina S. Udaltsova, Andreas Zezas and Vinay L. Kashyap --|tTechniques for Massive-Data Machine Learning in Astronomy /|rNicholas M. Ball --|tA Bayesian Approach to Gravitational Lens Model Selection /|rIrene Balmès --|tIdentification of Outliers Through Clustering and Semi-supervised Learning for All Sky Surveys /|rSharmodeep Bhattacharyya, Joseph W. Richards, John Rice, Dan L. Starr and Nathaniel R. Butler, et al. --|tEstimation of Moments on the Sphere by Means of Fast Convolution /|rP. Bielewicz, B.D. Wandelt and A.J. Banday --|tVariability Detection by Change-Point Analysis /|rSeo-Won Chang, Yong-Ik Byun and Jaegyoon Hahm --|tEvolution as a Confounding Parameter in Scaling Relations for Galaxies /|rDidier Fraix-Burnet --|tDetecting Galaxy Mergers at High Redshift /|rP.E. Freeman, R. Izbicki, Ann B. Lee, C. Schafer and D. Slepčev, et al.
505 80|tMulti-component Analysis of a Sample of Bright X-Ray Selected Active Galactic Nuclei /|rDirk Grupe --|tApplying the Background-Source Separation Algorithm to Chandra Deep Field South Data /|rF. Guglielmetti, H. Böhringer, R. Fischer, P. Rosati and P. Tozzi --|tNon-Gaussian Physics of the Cosmological Genus Statistic /|rJ. Berian James --|tModeling Undetectable Flares /|rVinay Kashyap, Steve Saar, Jeremy Drake, Kathy Reeves and Jennifer Posson-Brown, et al. --|tAn F-Statistic Based Multi-detector Veto for Detector Artifacts in Gravitational Wave Data /|rD. Keitel, R. Prix, M.A. Papa and M. Siddiqi --|tConstrained Probability Distributions of Correlation Functions /|rD. Keitel and P. Schneider --|tImproving Weak Lensing Reconstructions in 3D Using Sparsity /|rAdrienne Leonard, François-Xavier Dupé and Jean-Luc Starck --|tBayesian Predictions from the Semi-analytic Models of Galaxy Formation /|rYu Lu, H.J. Mo, Martin D. Weinberg and Neal Katz --|tStatistical Issues in Galaxy Cluster Cosmology /|rAdam Mantz, Steven W. Allen and David Rapetti.
505 80|tStatistical Analyses to Understand the Relationship Between the Properties of Exoplanets and Their Host Stars /|rElizabeth Martínez-Gómez --|tIdentifying High-z Gamma-Ray Burst Candidates Using Random Forest Classification /|rAdam N. Morgan, James Long, Tamara Broderick, Joseph W. Richards and Joshua S. Bloom --|tFitting Distributions of Points Using [tau]2 /|rTim Naylor --|tTheoretical Power Spectrum Estimation from Cosmic Microwave Background Data /|rPaniez Paykari, Jean-Luc Starck and M. Jalal Fadili --|tGuilt by Association: Finding Cosmic Ray Sources Using Hierarchical Bayesian Clustering /|rKunlaya Soiaporn, David Chernoff, Thomas Loredo, David Ruppert and Ira Wasserman --|tStatistical Differences Between Swift Gamma-Ray Burst Classes Based on [gamma]- and X-ray Observations /|rDorottya Szécsi, Lajos G. Balázs, Zsolt Bagoly, István Horváth and Attila Mészáros, et al. --|tA Quasi-Gaussian Approximation for the Probability Distribution of Correlation Functions /|rPhilipp Wilking and Peter Schneider --|tNew Insights into Galaxy Structure from GALPHAT /|rIlsang Yoon, Martin Weinberg and Neal Katz.
520 |aNow 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.
650 0|aStatistical astronomy|vCongresses.
650 0|aData mining|vCongresses.
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650 12|aData Mining.|0(DNLM)D057225
650 12|aStatistics
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655 4|aElectronic books.
655 2|aCongress.|0(DNLM)D016423
655 7|aConference papers and proceedings.|2fast|0(OCoLC)fst01423772
700 1 |aFeigelson, Eric D.
700 1 |aBabu, Gutti Jogesh,|d1949-
776 08|iPrint version:|w(OCoLC)775417676|w(OCoLC)811423003|w(OCoLC)828640425
830 0|aLecture notes in statistics (Springer-Verlag).|pProceedings ;|v209.
852 8 |beresour-nc|hOnline Resource|t1|zAccessible anywhere on campus or with UIUC NetID
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Staff View for: Statistical challenges in modern astrono