LEADER 04323nam 2200661Ia 450 001 9910453269603321 005 20200520144314.0 010 $a1-281-95005-X 010 $a9786611950057 010 $a0-387-73194-6 024 7 $a10.1007/978-0-387-73194-0 035 $a(CKB)1000000000546344 035 $a(EBL)417346 035 $a(OCoLC)317883311 035 $a(SSID)ssj0000251028 035 $a(PQKBManifestationID)11200066 035 $a(PQKBTitleCode)TC0000251028 035 $a(PQKBWorkID)10247133 035 $a(PQKB)11008430 035 $a(DE-He213)978-0-387-73194-0 035 $a(MiAaPQ)EBC417346 035 $a(PPN)132863189 035 $a(Au-PeEL)EBL417346 035 $a(CaPaEBR)ebr10273503 035 $a(CaONFJC)MIL195005 035 $a(EXLCZ)991000000000546344 100 $a20070907d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical decision theory$b[electronic resource] $eestimation, testing, and selection /$fFriedrich Liese, Klaus-J. Miescke 205 $a1st ed. 2008. 210 $aNew York $cSpringer$dc2008 215 $a1 online resource (695 p.) 225 1 $aSpringer series in statistics 300 $aDescription based upon print version of record. 311 $a0-387-73193-8 320 $aIncludes bibliographical references (p. [640]-662) and indexes. 327 $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. 330 $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. 410 0$aSpringer series in statistics. 606 $aStatistical decision 606 $aMathematical statistics 608 $aElectronic books. 615 0$aStatistical decision. 615 0$aMathematical statistics. 676 $a519.5/42 700 $aLiese$b Friedrich$f1944-$0890366 701 $aMiescke$b Klaus-J$0890367 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453269603321 996 $aStatistical decision theory$91988963 997 $aUNINA