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Full Description
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.
