{"product_id":"a-course-in-large-sample-and-high-dimensional-theory-hardcover","title":"A Course in Large-Sample and High-Dimensional Theory - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eZhiqiang Tan\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eClassical 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.\u003c\/p\u003e\u003cp\u003eAssuming 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 useful resource for those wishing to study classical asymptotics and modern high-dimensional theory as cohesive parts of a broader statistical framework.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey Features: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eFocuses on core, representative topics in classical and modern statistical theory, emphasizing essential ideas that help readers extend their understanding to related areas\u003c\/li\u003e \u003cli\u003eTreats important results that are otherwise scattered across research papers and monographs in a coherent and carefully organized manner\u003c\/li\u003e \u003cli\u003eProvides direct, self-contained proofs of main results while assuming only basic concepts and results from probability and real analysis\u003c\/li\u003e \u003cli\u003eReinforces learning with end-of-chapter exercises as well as questions and exercises integrated into the main text\u003c\/li\u003e \u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eZhiqiang Tan \u003c\/b\u003eis a Distinguished Professor in the Department of Statistics at Rutgers University. His research and teaching interests include Monte Carlo methods, causal inference, statistical learning, and related areas. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 234\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.63 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e August 10, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":49043025395961,"sku":"9781041153573","price":194.4,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/NvHJW7Vxzv9781041153573.webp?v=1789124498","url":"https:\/\/bookscloud.io\/products\/a-course-in-large-sample-and-high-dimensional-theory-hardcover","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}