A Course in Large-Sample and High-Dimensional Theory (Chapman & Hall/CRC Texts in Statistical Science)

This book provides a systematic treatment of two central regimes in statistical theory: classical large-sample theory for M- and Z-estimation with a fixed number of parameters, and high-dimensional theory where the number of parameters can be comparable to or larger than the sample size.While the former was developed earlier and remains fundamental, high-dimensional statistical theory has become an indispensable part of modern statistics. Classical large-sample theory and high-dimensional theory are typically compartmentalized into separate books and courses, which can make it difficult for readers to see how they relate.To foster learning, this book brings them together in a compact and integrated manner, highlighting both their differences and their shared underlying structures. Assuming a basic knowledge of mathematics and statistics, the book is intended primarily as a graduate textbook for students and researchers in Statistics, Data Science, and related fields.It serves as a usef