IISA Series on Statistics and Data Science – serie

This edited volume is a Data Science text, where multiple aspects of Statistics, Machine Learning, Artificial Intelligence, Big Data methodology, High-dimensional techniques and algorithms, and applications and case studies are presented together. Owing to its very broad scope,  the chapters of this book will be collected under thematically coherent groups. The planned groups of chapters are on (i) Regularization and high-dimensional machine learning, (ii) Bayesian high-dimensional modeling and computation, (iii) Spatio-temporal and dependent data models, and (iv) Deep learning and artificial intelligence. Case studies and applications, as well as high-dimensional probability theory may be two other groups of chapters, if a number of authors write with primary focus on these topics. This book will be useful for graduate students who want to specialize eventually on some aspect of Data Science, to beginners as well as advanced researchers in the field of Data Science, and mayas well serve as an encyclopedic text on Data Science.

Cancer clinical trials have become increasingly complex, requiring statistical methodologies that are both rigorous and flexible across diverse study designs. This book provides a comprehensive and practice-oriented overview of the statistical methods underpinning modern oncology drug development, covering the full continuum from early-phase studies through confirmatory trials and regulatory submission.Organized by development phase, the text presents principled approaches to dose-escalation and dose-optimization, proof-of-concept decision-making, and master protocol designs. It further details methodologies for late-stage trials, including sample size determination, group-sequential monitoring, time-to-event analysis, multiplicity adjustment, and adaptive designs, with particular attention to challenges such as delayed treatment effects.In addition to confirmatory trial methodology, the book addresses advanced analytical topics, including subgroup evaluation, treatment switching, multi-phase treatment strategies, and bias adjustment techniques. Contemporary issues in oncology research—such as the estimand framework, real-world evidence, seamless and platform trials, and emerging applications of artificial intelligence and machine learning—are also discussed.Accessible yet rigorous, this book is an essential resource for biostatisticians, clinical researchers, and graduate students who want to design smarter trials, make better decisions, and accelerate the development of life-saving cancer therapies.

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.