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Perspectives on big data analysis : [electronic resource] methodologies and applications : International Workshop on Perspectives on High-Dimension Data Anlaysis II, May 30-June 1, 2012, Centre de recherches math�ematiques, University de Montr�eal, Montr�eal, Qu�ebec, Canada / S. Ejaz Ahmed, editor.

By: (2nd : International Workshop on Perspectives on High-Dimension Data Anlaysis (2nd : 2012 : Montr�eal, Qu�ebec).

Contributor(s): Ahmed, S. E. (Syed Ejaz), 1957- [editor of compilation.].

Material type: materialTypeLabel

BookSeries: Contemporary mathematics, v. 622.Publisher: Providence, Rhode Island : American Mathematical Society, 2014Description: 1 online resource (pages cm.).Content type: text Media type: unmediated Carrier type: volumeISBN: 9781470418878 (online).Subject(s): Multivariate analysis -- Congresses | Artificial intelligence -- Congresses | Big data -- Congresses | Computer science -- Artificial intelligence -- None of the above, but in this section | Statistics -- Multivariate analysis -- Factor analysis and principal components; correspondence analysis | Statistics -- Linear inference, regression -- Ridge regression; shrinkage estimators | Statistics -- Parametric inference -- Asymptotic properties of tests | Statistics -- Nonparametric inference -- Estimation | Statistics -- Inference from stochastic processes -- Markov processes: estimation | Statistics -- Nonparametric inference -- Nonparametric regression | Probability theory and stochastic processes | Statistics -- Nonparametric inference -- None of the above, but in this section | Statistics -- Multivariate analysis -- Hypothesis testingAdditional physical formats: Perspectives on big data analysis :DDC classification: 519.5/35 Other classification: 68T99 | 62H25 | 62J07 | 62F05 | 62G05 | 62M05 | 62G08 | 60-XX | 62G99 | 62H15 Online resources: Contents | Contents

Contents:

Principal Component Analysis (PCA) for high-dimensional data. PCA is dead. Long live PCA / Fan Yang, Kjell Doksum and Kam-Wah Tsui -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12430 Solving a System of High-Dimensional Equations by MCMC / Nozer D. Singpurwalla and Joshua Landon -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12437 A slice sampler for the hierarchical Poisson/Gamma random field model / Jian Kang and Timothy D. Johnson -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12434 A new penalized quasi-likelihood approach for estimating the number of states in a hidden Markov model / Annaliza McGillivray and Abbas Khalili -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12435 Efficient adaptive estimation strategies in high-dimensional partially linear regression models / Xiaoli Gao and S. Ejaz Ahmed -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12441 Geometry and properties of generalized ridge regression in high dimensions / Hemant Ishwaran and J. Sunil Rao -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12438 Multiple testing for high-dimensional data / Guoqing Diao, Bret Hanlon and Anand N. Vidyashankar -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12440 On multiple contrast tests and simultaneous confidence intervals in high-dimensional repeated measures designs / Frank Konietschke, Yulia R. Gel and Edgar Brunner -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12431 Data-driven smoothing can preserve good asymptotic properties / Zhouwang Yang, Huizhi Xie and Xiaoming Huo -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12433 Variable selection for ultra-high-dimensional logistic models / Pang Du, Pan Wu and Hua Liang -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12436 Shrinkage estimation and selection for a logistic regression model / Shakhawat Hossain and S. Ejaz Ahmed -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12432 Manifold unfolding by Isometric Patch Alignment with an application in protein structure determination / Pooyan Khajehpour Tadavani, Babak Alipanahi and Ali Ghodsi -- http://www.ams.org/conm/622/ http://dx.doi.org/10.1090/conm/622/12429

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Includes bibliographical references and index.

Principal Component Analysis (PCA) for high-dimensional data. PCA is dead. Long live PCA / Fan Yang, Kjell Doksum and Kam-Wah Tsui -- Solving a System of High-Dimensional Equations by MCMC / Nozer D. Singpurwalla and Joshua Landon -- A slice sampler for the hierarchical Poisson/Gamma random field model / Jian Kang and Timothy D. Johnson -- A new penalized quasi-likelihood approach for estimating the number of states in a hidden Markov model / Annaliza McGillivray and Abbas Khalili -- Efficient adaptive estimation strategies in high-dimensional partially linear regression models / Xiaoli Gao and S. Ejaz Ahmed -- Geometry and properties of generalized ridge regression in high dimensions / Hemant Ishwaran and J. Sunil Rao -- Multiple testing for high-dimensional data / Guoqing Diao, Bret Hanlon and Anand N. Vidyashankar -- On multiple contrast tests and simultaneous confidence intervals in high-dimensional repeated measures designs / Frank Konietschke, Yulia R. Gel and Edgar Brunner -- Data-driven smoothing can preserve good asymptotic properties / Zhouwang Yang, Huizhi Xie and Xiaoming Huo -- Variable selection for ultra-high-dimensional logistic models / Pang Du, Pan Wu and Hua Liang -- Shrinkage estimation and selection for a logistic regression model / Shakhawat Hossain and S. Ejaz Ahmed -- Manifold unfolding by Isometric Patch Alignment with an application in protein structure determination / Pooyan Khajehpour Tadavani, Babak Alipanahi and Ali Ghodsi --

http://www.ams.org/conm/622/

http://dx.doi.org/10.1090/conm/622/12430

http://www.ams.org/conm/622/

http://dx.doi.org/10.1090/conm/622/12437

http://www.ams.org/conm/622/

http://dx.doi.org/10.1090/conm/622/12434

http://www.ams.org/conm/622/

http://dx.doi.org/10.1090/conm/622/12435

http://www.ams.org/conm/622/

http://dx.doi.org/10.1090/conm/622/12441

http://www.ams.org/conm/622/

http://dx.doi.org/10.1090/conm/622/12438