Focusing on theoretical advancements in logistic distribution, this book illustrates practical applications and details both univariate and multivariate generalizations. It serves as a valuable resource for researchers, practicing statisticians, and graduate students, providing insights into the utility of these statistical concepts.
Focusing on bivariate distributions, this book explores the dependence structures of random variables, emphasizing their correlation and properties for clearer understanding. It simplifies the joint density plot representation, making complex concepts more accessible. Additionally, the text discusses how bivariate distributions can be extended to multivariate forms using vector or matrix representations. This edition revises the first 17 chapters of the author's prior work, enhancing clarity and depth in the study of statistical dependencies.
The book provides a comprehensive guide to the theory and methods of progressive censoring, covering foundational concepts and the properties of progressively censored samples. It includes newly simulated data sets that illustrate key ideas, making complex theories accessible and practical for readers.
This volume offers a comprehensive look at parametric and semiparametric models, focusing on their applications in reliability, survival analysis, and quality of life. Featuring expert-written survey articles, it covers both general theory and specific advancements in real-world contexts.
This volume honors S. Panchapakesan's contributions to statistics, featuring twenty invited articles on topics like inference, ranking and selection, reliability, and biostatistics. The chapters include theory, methods, and applications, making it a valuable resource for researchers, practitioners, and graduate students in statistics and applied mathematics.