Frontiers of Statistics and Data Science
Language: English
Published by Springer, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 169.70
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - This book addresses a diverse set of topics of contemporary interest in statistics and data science such as biostatistics and machine learning. Each chapter provides an overview of the topic under discussion, so that any reader with an understanding of graduate-level statistics, but not necessarily with a prior background on the topic should be able to get a summary of developments in the field. These chapters serve as basic introductory references for new researchers in these fields, as well as the basis of teaching a course on the topic, or with a part of the course on topics of precision medicine, deep learning, high-dimensional central limit theorems, multivariate rank testing, R programming for statistics, Bayesian nonparametrics, large deviation asymptotics,spatio-temporal modeling of Covid-19, statistical network models,hidden Markov models, statistical record linkage analysis. The edited volume will be most useful for graduate students looking for an overview of any of the covered topics for their research and for instructors for developing certain courses by including any of the topics as part of the course. Students enrolled in a course covering any of the included topics can also benefit from these chapters.…
Seller Inventory # 9789819607440
- Title
- Frontiers of Statistics and Data Science
- Author
- Subhashis Ghosal
- Publisher
- Springer
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9819607442
- ISBN 13
- 9789819607440
- Item weight
- 359 grams
- Dimensions
- 235x155x13 mm
This book addresses a diverse set of topics of contemporary interest in statistics and data science such as biostatistics and machine learning. Each chapter provides an overview of the topic under discussion, so that any reader with an understanding of graduate-level statistics, but not necessarily with a prior background on the topic should be able to get a summary of developments in the field. These chapters serve as basic introductory references for new researchers in these fields, as well as the basis of teaching a course on the topic, or with a part of the course on topics of precision medicine, deep learning, high-dimensional central limit theorems, multivariate rank testing, R programming for statistics, Bayesian nonparametrics, large deviation asymptotics, spatio-temporal modeling of Covid-19, statistical network models, hidden Markov models, statistical record linkage analysis. The edited volume will be most useful for graduate students looking for an overview of any of the covered topics for their research and for instructors for developing certain courses by including any of the topics as part of the course. Students enrolled in a course covering any of the included topics can also benefit from these chapters.
"Synopsis" may belong to another edition of this title.
About the Author
Subhashis Ghoshal is the Goodnight Distinguished Professor of Statistics at North Carolina State University. He is a Fellow of IMS, ASA and ISBA, and winner of DeGroot Prize for the best book in Statistical Science, 2019, for his book “Fundamentals of Nonparametric Bayesian Inference”. He has written over 150 research articles and advised over 30 doctoral students. He has a variety of research interests including Bayesian inference, nonparametrics, high-dimensional statistics, differential equation models, image processing, and others. His research has been funded NSF, ARO, NSA, Samsung and others, including the prestigious NSF Career Award.
Anindya Roy is a Professor of Statistics at the University of Maryland, Baltimore County. He also holds an appointment with the US Census Bureau. He is a Fellow of ASA, and author of a popular textbook “Linear Algebra and Matrix Analysis for Statistics”. His research interests include time series analysis, Bayesian statistics, high-dimensional statistics, data confidentiality, and others. He has written over 80 research articles and advised over 25 doctoral students. His research has been funded by NSF, NIH, and other funding agencies.
"About the title" may belong to another edition of this title.
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