000			03386nam a22004335i 4500
001			978-0-387-27605-2
003			DE-He213
005			20250710083939.0
007			cr nn 008mamaa
008			100301s2005 xxu| s |||| 0|eng d
020			_a9780387276052 _a99780387276052
024	7		_a10.1007/0-387-27605-X _2doi
082	0	4	_a519.5 _223
100	1		_aLehmann, E. L. _eauthor.
245	1	0	_aTesting Statistical Hypotheses _h[recurso electrónico] / _cby E. L. Lehmann, Joseph P. Romano.
250			_a3.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_aXIV, 786 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Texts in Statistics, _x1431-875X
505	0		_aSmall-Sample Theory -- The General Decision Problem -- The Probability Background -- Uniformly Most Powerful Tests -- Unbiasedness: Theory and First Applications -- Unbiasedness: Applications to Normal Distributions; Confidence Intervals -- Invariance -- Linear Hypotheses -- The Minimax Principle -- Multiple Testing and Simultaneous Inference -- Conditional Inference -- Large-Sample Theory -- Basic Large Sample Theory -- Quadratic Mean Differentiable Families -- Large Sample Optimality -- Testing Goodness of Fit -- General Large Sample Methods.
520			_aThe third edition of Testing Statistical Hypotheses updates and expands upon the classic graduate text, emphasizing optimality theory for hypothesis testing and confidence sets. The principal additions include a rigorous treatment of large sample optimality, together with the requisite tools. In addition, an introduction to the theory of resampling methods such as the bootstrap is developed. The sections on multiple testing and goodness of fit testing are expanded. The text is suitable for Ph.D. students in statistics and includes over 300 new problems out of a total of more than 760. E.L. Lehmann is Professor of Statistics Emeritus at the University of California, Berkeley. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and the recipient of honorary degrees from the University of Leiden, The Netherlands and the University of Chicago. He is the author of Elements of Large-Sample Theory and (with George Casella) he is also the author of Theory of Point Estimation, Second Edition. Joseph P. Romano is Professor of Statistics at Stanford University. He is a recipient of a Presidential Young Investigator Award and a Fellow of the Institute of Mathematical Statistics. He has coauthored two other books, Subsampling with Dimitris Politis and Michael Wolf, and Counterexamples in Probability and Statistics with Andrew Siegel.
650		0	_aSTATISTICS.
650		0	_aMATHEMATICAL STATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
700	1		_aRomano, Joseph P. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387988641
830		0	_aSpringer Texts in Statistics, _x1431-875X
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-27605-X _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c56711 _d56711