{"product_id":"large-sample-techniques-for-statistics-paperback","title":"Large Sample Techniques for Statistics - Paperback","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\u003eJiming Jiang\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eThis book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, \u003ci\u003eLarge Sample Techniques for Statistics\u003c\/i\u003e begins with fundamental techniques, and connects theory and applications in engaging ways.\u003cbr\u003eThe first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. \u003cbr\u003eThe book's case studies and applications-oriented chapters demonstrate how to use methods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science.\u003cbr\u003eThis text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites..\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eThis book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, \u003ci\u003eLarge Sample Techniques for Statistics\u003c\/i\u003e begins with fundamental techniques, and connects theory and applications in engaging ways.\u003cbr\u003eThe first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. \u003cbr\u003eThe book's case studies and applications-oriented chapters demonstrate how to usemethods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science.\u003cbr\u003eThis text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites..\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003cb\u003eJiming Jiang\u003c\/b\u003e is Professor of Statistics and a former Director of Statistical Laboratory at the University of California, Davis. He is a prominent researcher in the fields of mixed effects models, small area estimation, model selection, and statistical genetics. He is the author of \u003ci\u003eLinear and Generalized Linear Mixed Models and Their Applications, 2nd Edition\u003c\/i\u003e (Springer 2021), \u003ci\u003eRobust Mixed Model Analysis\u003c\/i\u003e (2019), \u003ci\u003eAsymptotic Analysis of Mixed Effects Models: Theory, Applications, and Open Problems\u003c\/i\u003e (2017), and \u003ci\u003eThe Fence Methods \u003c\/i\u003e(with T. Ngyuen, 2016). Jiming Jiang has been editorial board member of \u003ci\u003eThe Annals of Statistics \u003c\/i\u003eand \u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, among others. He is a Fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics; an elected member of the International Statistical Institute; and a Yangtze River Scholar (Chaired Professor, 2017-2020).\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 685\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.41 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 April 06, 2023\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":45673150382124,"sku":"9783030916978","price":129.58,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0728\/0954\/5772\/files\/Kkg4SPw7Oy9783030916978.webp?v=1781720852","url":"https:\/\/smartsupplydeals.com\/products\/large-sample-techniques-for-statistics-paperback","provider":"Smart supply deals","version":"1.0","type":"link"}