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001			278367
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008			150903s2005 xxu| o |||| 0|eng d
020			_a9780387276052 _9978-0-387-27605-2
024	7		_a10.1007/038727605-X _2doi
035			_avtls000330431
039		9	_a201509030201 _bVLOAD _c201404120442 _dVLOAD _c201404090224 _dVLOAD _c201401311340 _dstaff _y201401291455 _zstaff _wmsplit0.mrc _x851
050		4	_aQA276-280
100	1		_aLehmann, E. L. _eautor _9301957
245	1	0	_aTesting Statistical Hypotheses / _cby E. L. Lehmann, Joseph P. Romano.
250			_a3.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_aXIV, 786 páginas, _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringer Texts in Statistics, _x1431-875X
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aRomano, Joseph P. _eautor _9301958
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387988641
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/0-387-27605-X _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c278367 _d278367