000 03767nam a22004455i 4500
001 978-1-4419-6827-2
003 DE-He213
005 20140220084510.0
007 cr nn 008mamaa
008 100702s2010 xxu| s |||| 0|eng d
020 _a9781441968272
_9978-1-4419-6827-2
024 7 _a10.1007/978-1-4419-6827-2
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aJiang, Jiming.
_eauthor.
245 1 0 _aLarge Sample Techniques for Statistics
_h[electronic resource] /
_cby Jiming Jiang.
250 _a1.
264 1 _aNew York, NY :
_bSpringer New York,
_c2010.
300 _aXVIII, 582p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Texts in Statistics,
_x1431-875X ;
_v0
505 0 _aThe ?-? 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 0 _aStatistics.
650 0 _aMathematical statistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441968265
830 0 _aSpringer Texts in Statistics,
_x1431-875X ;
_v0
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-6827-2
912 _aZDB-2-SMA
999 _c110695
_d110695