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020 _a9780387731940
_99780387731940
024 7 _a10.1007/9780387731940
_2doi
035 _avtls000332351
039 9 _a201509030735
_bVLOAD
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040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
100 1 _aMiescke, Klaus-J.
_eautor
_9302443
245 1 0 _aStatistical Decision Theory :
_bEstimation, Testing, and Selection /
_cby Klaus-J. Miescke, F. Liese.
264 1 _aNew York, NY :
_bSpringer New York,
_c2008.
300 _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 Series in Statistics,
_x0172-7397
500 _aSpringer eBooks
505 0 _aStatistical Models -- Tests in Models with Monotonicity Properties -- Statistical Decision Theory -- Comparison of Models, Reduction by -- Invariant Statistical Decision Models -- Large Sample Approximations of Models and Decisions -- Estimation -- Testing -- Selection.
520 _aThis monograph is written for advanced graduate students, Ph.D. students, and researchers in mathematical statistics and decision theory. All major topics are introduced on a fairly elementary level and then developed gradually to higher levels. The book is self-contained as it provides full proofs, worked-out examples, and problems. It can be used as a basis for graduate courses, seminars, Ph.D. programs, self-studies, and as a reference book. The authors present a rigorous account of the concepts and a broad treatment of the major results of classical finite sample size decision theory and modern asymptotic decision theory. Highlights are systematic applications to the fields of parameter estimation, testing hypotheses, and selection of populations. With its broad coverage of decision theory that includes results from other more specialized books as well as new material, this book is one of a kind and fills the gap between standard graduate texts in mathematical statistics and advanced monographs on modern asymptotic theory. One goal is to present a bridge from the classical results of mathematical statistics and decision theory to the modern asymptotic decision theory founded by LeCam. The striking clearness and powerful applicability of LeCam’s theory is demonstrated with its applications to estimation, testing, and selection on an intermediate level that is accessible to graduate students. Another goal is to present a broad coverage of both the frequentist and the Bayes approach in decision theory. Relations between the Bayes and minimax concepts are studied, and fundamental asymptotic results of modern Bayes statistical theory are included. The third goal is to present, for the first time in a book, a well-rounded theory of optimal selections for parametric families. Friedrich Liese, University of Rostock, and Klaus-J. Miescke, University of Illinois at Chicago, are professors of mathematical statistics who have published numerous research papers in mathematical statistics and decision theory over the past three decades.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aLiese, F.
_eautor
_9302444
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780387731933
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-73194-0
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c278669
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