000			03199nam a22002777a 4500
005			20220408150839.0
008			220408b |||||||| |||| 00| 0 eng d
020			_a9781461426233
028			_a:Zafaa Books & Distributors _b:313/56F 49A, Anand Nagar Inderlok Delhi 110035 _c:ZBD0019 _d:04/04/2022
082			_a519.52 JIA
100			_aJiang, Jiming
245			_aLarge Sample Techniques For Statistics _c/ Jiming Jiang
260			_aNew York _b: Springer, _c ©2010.
300			_axvii, 609p _c24cm.
365			_b7119
490			_aSpringer texts in statistics.
505			_a The?-? Arguments -- Modes of Convergence -- Big O, Small o, and the Unspecified c -- Asymptotic Expansions -- Inequalities -- Sums of Independent Random Variables -- Empirical Processes -- Martingales -- Time and Spatial Series -- Stochastic Processes -- Nonparametric Statistics -- Mixed Effects Models -- Small-Area Estimation -- Jackknife and Bootstrap -- Markov-Chain Monte Carlo.
520			_aThis book offers a comprehensive guide to large sample techniques in statistics. More importantly, it focuses on thinking skills rather than just what formulae to use; it provides motivations, and intuition, rather than detailed proofs; it begins with very simple techniques, and connects theory and applications in entertaining ways. The 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 10 chapters contains at least one section of case study. The last five chapters are devoted to special areas of applications. The sections of case studies and chapters of applications fully demonstrate how to use methods developed from large sample theory in various, less-than-textbook situations. The book is supplemented by a large number of exercises, giving the readers plenty of opportunities to practice what they have learned. The book is mostly self-contained with the appendices providing some backgrounds for matrix algebra and mathematical statistics. The book is intended for a wide audience, ranging from senior undergraduate students to researchers with Ph. D. degrees. A first course in mathematical statistics and a course in calculus are prerequisites. Jiming Jiang is a Professor of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a Fellow of the Institute of Mathematical Statistics. He is the author of another Springer book, Linear and Generalized Linear Mixed Models and Their Applications (2007). Jiming Jiang is a prominent researcher in the fields of mixed effects models, small area estimation and model selection. Most of his research papers have involved large sample techniques. He is currently an Associate Editor of the Annals of Statistics
650			_aMathematics
650			_aProbabilities and applied mathematics
650			_aSampling Statistics
650			_aTime and Spatial Series
650			_aStochastic Process
700			_aJiang, Jiming
942			_cBOOK
999			_c59093 _d59093