The Springer Series in Statistics includes a collection of books on statistical methods and theories. Notable titles include "Statistical Models Based on Counting Processes" by Per Kragh Andersen, Niels Keiding, Ørnulf Borgan, and Richard D. Gill. This book focuses on the application of counting processes, martingales, and stochastic integration in event history analysis. It covers topics such as survival analysis, censoring, truncation, and the use of intensities in modeling. The book presents a non- and semi-parametric theory, with one chapter dedicated to parametric models. It includes practical examples from biostatistical research and discusses recent developments in the field. The authors aim to provide a comprehensive theory for handling statistical problems in continuous time, with a balance between mathematical rigor and practical applications. The book is written for a broad audience, including researchers and students in statistics, and includes a detailed plan of the book in Chapter I. The authors also mention the contributions of various colleagues and the support received from institutions. The book is divided into several chapters, covering topics such as mathematical background, model specification and censoring, nonparametric estimation, hypothesis testing, parametric models, regression models, asymptotic efficiency, frailty models, and multivariate time scales. The book includes appendices, references, and indexes for further reading. The authors acknowledge the support and hospitality received from various institutions and individuals throughout the writing process. The book is intended to serve as a comprehensive reference for researchers and practitioners in the field of statistics.The Springer Series in Statistics includes a collection of books on statistical methods and theories. Notable titles include "Statistical Models Based on Counting Processes" by Per Kragh Andersen, Niels Keiding, Ørnulf Borgan, and Richard D. Gill. This book focuses on the application of counting processes, martingales, and stochastic integration in event history analysis. It covers topics such as survival analysis, censoring, truncation, and the use of intensities in modeling. The book presents a non- and semi-parametric theory, with one chapter dedicated to parametric models. It includes practical examples from biostatistical research and discusses recent developments in the field. The authors aim to provide a comprehensive theory for handling statistical problems in continuous time, with a balance between mathematical rigor and practical applications. The book is written for a broad audience, including researchers and students in statistics, and includes a detailed plan of the book in Chapter I. The authors also mention the contributions of various colleagues and the support received from institutions. The book is divided into several chapters, covering topics such as mathematical background, model specification and censoring, nonparametric estimation, hypothesis testing, parametric models, regression models, asymptotic efficiency, frailty models, and multivariate time scales. The book includes appendices, references, and indexes for further reading. The authors acknowledge the support and hospitality received from various institutions and individuals throughout the writing process. The book is intended to serve as a comprehensive reference for researchers and practitioners in the field of statistics.