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Perspectives on big data analysis: methodologies and applications : centre de recherches mathematiques proceedings / [edited by] S. Ejaz Ahmed.

By: Contributor(s): Material type: TextTextSeries: Contemporary mathematics ; 622.Publication details: Providence : American Mathematical Society, c2014.Description: xi, 191 p. : illustrations ; 26 cmISBN:
  • 9781470410421 (alk. paper)
Other title:
  • Big data analysis [Spine title]
Subject(s): DDC classification:
  • 510 23 Am512c
Contents:
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 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.
Summary: This volume contains the proceedings of the International Workshop on Perspectives on High-dimensional Data Analysis II, held May 30-June 1, 2012, at the Centre de Recherches Mathematiques, Universite de Montreal, Montreal, Quebec, Canada. This book collates applications and methodological developments in high-dimensional statistics dealing with interesting and challenging problems concerning the analysis of complex, high-dimensional data with a focus on model selection and data reduction. The chapters contained in this book deal with submodel selection and parameter estimation for an array of interesting models. The book also presents some surprising results on high-dimensional data analysis, especially when signals cannot be effectively separated from the noise, it provides a critical assessment of penalty estimation when the model may not be sparse, and it suggests alternative estimation strategies. Readers can apply the suggested methodologies to a host of applications and also can extend these methodologies in a variety of directions. This volume conveys some of the surprises, puzzles and success stories in big data analysis and related fields. This book is co-published with the Centre de Recherches Mathematiques.
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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 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.

This volume contains the proceedings of the International Workshop on Perspectives on High-dimensional Data Analysis II, held May 30-June 1, 2012, at the Centre de Recherches Mathematiques, Universite de Montreal, Montreal, Quebec, Canada. This book collates applications and methodological developments in high-dimensional statistics dealing with interesting and challenging problems concerning the analysis of complex, high-dimensional data with a focus on model selection and data reduction. The chapters contained in this book deal with submodel selection and parameter estimation for an array of interesting models. The book also presents some surprising results on high-dimensional data analysis, especially when signals cannot be effectively separated from the noise, it provides a critical assessment of penalty estimation when the model may not be sparse, and it suggests alternative estimation strategies. Readers can apply the suggested methodologies to a host of applications and also can extend these methodologies in a variety of directions. This volume conveys some of the surprises, puzzles and success stories in big data analysis and related fields. This book is co-published with the Centre de Recherches Mathematiques.

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