1st Edition
A Course in the Large Sample Theory of Statistical Inference
Random Variables and Vectors Page. Weak Convergence Page. Asymptotic Linearity of Statistics Page. Local Analysis Page. Large-Sample Estimation Page. Large-Sample Hypothesis Testing and Confidence Sets Page. An Introduction to Rank Tests and Estimates Page. An Introduction to Multinomial Chi-square Tests Page.
Biography
W. J. (“Jack”) Hall was Professor at the University of Rochester from 1969 to his death in 2012. He was instrumental in founding the graduate program in Statistics. His research interests included decision theory, survival analysis, semiparametric inference and sequential analysis. He worked with medical colleagues to develop innovative statistical designs for clinical trials in cardiology.
David Oakes is Professor and a former department chair at the University of Rochester. His areas of research interests include survival analysis and stochastic processes.
"Overall, the book is presented clearly, with an excellent sequence of concepts that guide the reader through the material effectively. I foundmost chapters engaging and detailed, offering a good balance of theory and application.[...] A Course in the Large Sample Theory of Statistical Inference is a comprehensive and accessible textbook, well-suited for a graduate-level course on large sample theory. Building on the concepts fromstandard/intermediate statistical inference courses, this book offers a smooth transition into the principles of large sample theory. It features simplified derivations of key results, along with numerical illustrations, practical examples, and insightful guidance. This combination provides a strong foundation for graduate students, researchers, and practitioners who seek to apply these concepts to real-worlddata applications. Certainly suitable for a library purchase, and, definitely worthy of my office shelf!"
-Indranil Sahoo, in The American Statistician, December 2024
