LEADER 01190nam0-22003611i-450- 001 990004626570403321 005 20110912163405.0 035 $a000462657 035 $aFED01000462657 035 $a(Aleph)000462657FED01 035 $a000462657 100 $a19990604g19521957km-y0itay50------ba 101 0 $ager 102 $aDE 105 $ay-------001ey 200 1 $aBriefe$fJohann Joachim Winckelmann$gin Verbindung mit Hans Diepolder hrsg. von Walther Rehm 210 $aBerlin$cde Gruyter$d1952-1957 215 $a4 v.$d24 cm 327 1 $a1.: 1742-1759. - 1952$a2.: 1759-1763. - 1952$a3.: 1764-1768. - 1956$a4.: Dokumente zur Lebengeschichte. - 1957 676 $a930.1 700 1$aWinckelmann,$bJohann Joachim$f<1717-1768>$07211 702 1$aDiepolder,$bHans 702 1$aRehm,$bWalther 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004626570403321 952 $a930.1 WIN 1 (1)$bBibl.33756$fFLFBC 952 $a930.1 WIN 1 (2)$bBibl.33757$fFLFBC 952 $aP2D-WINCKELMANN J.J. (3)-1956$bBibl.33758$fFLFBC 952 $aP2D-WINCKELMANN J.J. (4)-1957$bBibl.33759$fFLFBC 959 $aFLFBC 996 $aBriefe$9552636 997 $aUNINA LEADER 01870nam 2200553I 450 001 9910705296303321 005 20140604114901.0 035 $a(CKB)5470000002448399 035 $a(OCoLC)880937280 035 $a(EXLCZ)995470000002448399 100 $a20140604j201308 ua 0 101 0 $aeng 135 $aurbn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe chorus conflict and loss of separation resolution algorithms /$fRicky W. Butler, George E. Hagen, and Jeffrey M. Maddalon 210 1$aHampton, Virginia :$cNational Aeronautics and Space Administration, Langley Research Center,$dAugust 2013. 215 $a1 online resource (iv, 30 pages) $cillustrations 225 1 $aNASA/TM ;$v2013-218030 300 $aTitle from title screen (viewed June 4, 2014). 300 $a"August 2013." 320 $aIncludes bibliographical references (pages 24-25). 606 $aAir traffic control$2nasat 606 $aAlgorithms$2nasat 606 $aC++ (programming language)$2nasat 606 $aComputer programs$2nasat 606 $aCoordination$2nasat 606 $aDetection$2nasat 606 $aDomains$2nasat 606 $aJava (programming language)$2nasat 615 7$aAir traffic control. 615 7$aAlgorithms. 615 7$aC++ (programming language) 615 7$aComputer programs. 615 7$aCoordination. 615 7$aDetection. 615 7$aDomains. 615 7$aJava (programming language) 700 $aButler$b Ricky W.$01398460 702 $aHagen$b George E. 702 $aMaddalon$b Jeffrey M. 712 02$aLangley Research Center, 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910705296303321 996 $aThe chorus conflict and loss of separation resolution algorithms$93488406 997 $aUNINA LEADER 03057nam 22006255 450 001 9910993944503321 005 20250316135535.0 010 $a9789819960774 010 $a9819960770 024 7 $a10.1007/978-981-99-6077-4 035 $a(MiAaPQ)EBC30761277 035 $a(Au-PeEL)EBL30761277 035 $a(DE-He213)978-981-99-6077-4 035 $a(CKB)28349519500041 035 $a(EXLCZ)9928349519500041 100 $a20230929d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStein Estimation /$fby Yuzo Maruyama, Tatsuya Kubokawa, William E. Strawderman 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (0 pages) 225 1 $aJSS Research Series in Statistics,$x2364-0065 311 08$a9789819960767 311 08$a9819960762 320 $aIncludes bibliographical references. 327 $a1. Decision Theory Preliminaries -- 2. Minimaxity and Improvement on the James-Stein estimator -- 3. Admissibility. 330 $aThis book provides a self-contained introduction of Stein/shrinkage estimation for the mean vector of a multivariate normal distribution. The book begins with a brief discussion of basic notions and results from decision theory such as admissibility, minimaxity, and (generalized) Bayes estimation. It also presents Stein's unbiased risk estimator and the James-Stein estimator in the first chapter. In the following chapters, the authors consider estimation of the mean vector of a multivariate normal distribution in the known and unknown scale case when the covariance matrix is a multiple of the identity matrix and the loss is scaled squared error. The focus is on admissibility, inadmissibility, and minimaxity of (generalized) Bayes estimators, where particular attention is paid to the class of (generalized) Bayes estimators with respect to an extended Strawderman-type prior. For almost all results of this book, the authors present a self-contained proof. The book is helpful for researchers and graduate students in various fields requiring data analysis skills as well as in mathematical statistics. 410 0$aJSS Research Series in Statistics,$x2364-0065 606 $aStatistics 606 $aStatistics 606 $aApplied Statistics 606 $aStatistical Theory and Methods 606 $aBayesian Inference 606 $aBayesian Network 615 0$aStatistics. 615 0$aStatistics. 615 14$aApplied Statistics. 615 24$aStatistical Theory and Methods. 615 24$aBayesian Inference. 615 24$aBayesian Network. 676 $a519.544 700 $aMaruyama$b Yuzo$01745419 702 $aKubokawa$b Tatsuya 702 $aStrawderman$b William E. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910993944503321 996 $aStein Estimation$94369860 997 $aUNINA