This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 232 pp. Englisch.

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T�tulo

Frontiers of Statistics and Data Science

Editor

Springer Nature Singapore, Springer Nature Singapore Jun 2025

Fecha de publicaci�n

2025

Idioma

Ingl�s

ISBN 10

9819607418

ISBN 13

9789819607419

Encuadernaci�n

Buch

Estado

Neu

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Sinopsis

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.

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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.

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