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.

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 --

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Electronic reproduction. Providence, Rhode Island : American Mathematical Society. 2014

Mode of access : World Wide Web

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