000			03233nam a22004215i 4500
001			978-0-387-93839-4
003			DE-He213
005			20251006084432.0
007			cr nn 008mamaa
008			100917s2010 xxu| s |||| 0|eng d
020			_a9780387938394
020			_a99780387938394
024	7		_a10.1007/978-0-387-93839-4 _2doi
082	0	4	_a519.5 _223
100	1		_aKeener, Robert W. _eauthor.
245	1	0	_aTheoretical Statistics _h[electronic resource] : _bTopics for a Core Course / _cby Robert W. Keener.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_aXVIII, 538 p. _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
505	0		_aProbability and Measure -- Exponential Families -- Risk, Sufficiency, Completeness, and Ancillarity -- Unbiased Estimation -- Curved Exponential Families -- Conditional Distributions -- Bayesian Estimation -- Large-Sample Theory -- Estimating Equations and Maximum Likelihood -- Equivariant Estimation -- Empirical Bayes and Shrinkage Estimators -- Hypothesis Testing -- Optimal Tests in Higher Dimensions -- General Linear Model -- Bayesian Inference: Modeling and Computation -- Asymptotic Optimality1 -- Large-Sample Theory for Likelihood Ratio Tests -- Nonparametric Regression -- Bootstrap Methods -- Sequential Methods.
520			_aIntended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential. The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis. The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix. Robert Keener is Professor of Statistics at the University of Michigan and a fellow of the Institute of Mathematical 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: _z9780387938387
830		0	_aSpringer Texts in Statistics, _x1431-875X
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-93839-4 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c59542 _d59542