Softcover ISBN: | 978-1-4704-1042-1 |
Product Code: | CONM/622 |
List Price: | $130.00 |
MAA Member Price: | $117.00 |
AMS Member Price: | $104.00 |
eBook ISBN: | 978-1-4704-1887-8 |
Product Code: | CONM/622.E |
List Price: | $125.00 |
MAA Member Price: | $112.50 |
AMS Member Price: | $100.00 |
Softcover ISBN: | 978-1-4704-1042-1 |
eBook: ISBN: | 978-1-4704-1887-8 |
Product Code: | CONM/622.B |
List Price: | $255.00 $192.50 |
MAA Member Price: | $229.50 $173.25 |
AMS Member Price: | $204.00 $154.00 |
Softcover ISBN: | 978-1-4704-1042-1 |
Product Code: | CONM/622 |
List Price: | $130.00 |
MAA Member Price: | $117.00 |
AMS Member Price: | $104.00 |
eBook ISBN: | 978-1-4704-1887-8 |
Product Code: | CONM/622.E |
List Price: | $125.00 |
MAA Member Price: | $112.50 |
AMS Member Price: | $100.00 |
Softcover ISBN: | 978-1-4704-1042-1 |
eBook ISBN: | 978-1-4704-1887-8 |
Product Code: | CONM/622.B |
List Price: | $255.00 $192.50 |
MAA Member Price: | $229.50 $173.25 |
AMS Member Price: | $204.00 $154.00 |
-
Book DetailsContemporary MathematicsCentre de Recherches Mathématiques ProceedingsVolume: 622; 2014; 191 ppMSC: 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.
This book is co-published with the Centre de Recherches Mathématiques.
ReadershipGraduate 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
-
RequestsReview Copy – for publishers of book reviewsAccessibility – to request an alternate format of an AMS title
- Book Details
- Table of Contents
- Additional Material
- Requests
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.
This book is co-published with the Centre de Recherches Mathématiques.
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