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Perspectives on Big Data Analysis: Methodologies and Applications
 
Edited by: S. Ejaz Ahmed Brock University, St. Catharines, Ontario, Canada
A co-publication of the AMS and Centre de Recherches Mathématiques
Front Cover for Perspectives on Big Data Analysis
Available Formats:
Softcover ISBN: 978-1-4704-1042-1
Product Code: CONM/622
List Price: $83.00
MAA Member Price: $74.70
AMS Member Price: $66.40
Electronic ISBN: 978-1-4704-1887-8
Product Code: CONM/622.E
List Price: $78.00
MAA Member Price: $70.20
AMS Member Price: $62.40
Bundle Print and Electronic Formats and Save!
This product is available for purchase as a bundle. Purchasing as a bundle enables you to save on the electronic version.
List Price: $124.50
MAA Member Price: $112.05
AMS Member Price: $99.60
Front Cover for Perspectives on Big Data Analysis
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  • Front Cover for Perspectives on Big Data Analysis
  • Back Cover for Perspectives on Big Data Analysis
Perspectives on Big Data Analysis: Methodologies and Applications
Edited by: S. Ejaz Ahmed Brock University, St. Catharines, Ontario, Canada
A co-publication of the AMS and Centre de Recherches Mathématiques
Available Formats:
Softcover ISBN:  978-1-4704-1042-1
Product Code:  CONM/622
List Price: $83.00
MAA Member Price: $74.70
AMS Member Price: $66.40
Electronic ISBN:  978-1-4704-1887-8
Product Code:  CONM/622.E
List Price: $78.00
MAA Member Price: $70.20
AMS Member Price: $62.40
Bundle Print and Electronic Formats and Save!
This product is available for purchase as a bundle. Purchasing as a bundle enables you to save on the electronic version.
List Price: $124.50
MAA Member Price: $112.05
AMS Member Price: $99.60
  • Book Details
     
     
    Contemporary Mathematics
    Centre de Recherches Mathématiques Proceedings
    Volume: 6222014; 191 pp
    MSC: Primary 68; 62; 60;

    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 Mathématiques, Université de Montréal, Montréal, 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.

    Readership

    Graduate students and research mathematicians interested in statistics and data analysis.

  • Table of Contents
     
     
    • Articles
    • Fan Yang, Kjell Doksum and Kam-Wah Tsui - Principal Component Analysis (PCA) for high-dimensional data. PCA is dead. Long live PCA
    • Nozer D. Singpurwalla and Joshua Landon - Solving a System of High-Dimensional Equations by MCMC
    • Jian Kang and Timothy D. Johnson - A slice sampler for the hierarchical Poisson/Gamma random field model
    • Annaliza McGillivray and Abbas Khalili - A new penalized quasi-likelihood approach for estimating the number of states in a hidden Markov model
    • Xiaoli Gao and S. Ejaz Ahmed - Efficient adaptive estimation strategies in high-dimensional partially linear regression models
    • Hemant Ishwaran and J. Sunil Rao - Geometry and properties of generalized ridge regression in high dimensions
    • Guoqing Diao, Bret Hanlon and Anand N. Vidyashankar - Multiple testing for high-dimensional data
    • Frank Konietschke, Yulia R. Gel and Edgar Brunner - On multiple contrast tests and simultaneous confidence intervals in high-dimensional repeated measures designs
    • Zhouwang Yang, Huizhi Xie and Xiaoming Huo - Data-driven smoothing can preserve good asymptotic properties
    • Pang Du, Pan Wu and Hua Liang - Variable selection for ultra-high-dimensional logistic models
    • Shakhawat Hossain and S. Ejaz Ahmed - Shrinkage estimation and selection for a logistic regression model
    • Pooyan Khajehpour Tadavani, Babak Alipanahi and Ali Ghodsi - Manifold unfolding by Isometric Patch Alignment with an application in protein structure determination
  • Additional Material
     
     
  • Request Review Copy
Centre de Recherches Mathématiques Proceedings
Volume: 6222014; 191 pp
MSC: Primary 68; 62; 60;

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 Mathématiques, Université de Montréal, Montréal, 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.

Readership

Graduate students and research mathematicians interested in statistics and data analysis.

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