Frontiers of Statistics and Data Science / edited by Subhashis Ghosal, Anindya Roy.

Format:

Book

Author/Creator:

Ghosal, Subhashis.

Contributor:

Roy, Anindya.

Series:

IISA Series on Statistics and Data Science, 2524-7492

Language:

English

Subjects (All):

Machine learning.

Biometry.

Artificial intelligence--Data processing.

Artificial intelligence.

Probabilities.

Machine Learning.

Biostatistics.

Data Science.

Applied Probability.

Local Subjects:

Machine Learning.

Biostatistics.

Data Science.

Applied Probability.

Physical Description:

1 online resource (374 pages)

Edition:

1st ed. 2025.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2025.

Summary:

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.

Contents:

Chapter 1: Artificial Intelligence in Precision Medicine and Digital Health

Chapter 2: Revisiting Doob’s Theorem on Posterior Consistency

Chapter 3: The Central Limit Theorem in High-dimension

Chapter 4: An Introduction to Deep Learning

Chapter 5: The R Language and its Use in Statistics

Chapter 6: Large Deviation Asymptotics for Systems with Fractional Noise

Chapter 7: High dimensional Wigner matrices with general independent entries

Chapter 8: Data Analysis after Record Linkage: Sources of Error, Consequences, and Possible Solutions

Chapter 9: Statistical Inference of Network Data: Past, Present, and Future

Chapter 10: Current topics in group testing.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9607-42-6

OCLC:

1524421933