LEADER 01406oam 2200409Ia 450 001 9910697159303321 005 20230902162202.0 035 $a(CKB)5470000002385318 035 $a(OCoLC)640105729 035 $a(EXLCZ)995470000002385318 100 $a20100519d2010 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPredicting morphology of polymers using Mesotek+$b[electronic resource] /$fby Tanya L. Chantawansri, Erin M. Lennon, Jan Andzelm 210 1$aAberdeen Proving Ground, MD :$cArmy Research Laboratory,$d[2010] 215 $a1 online resource (vi, 22 pages) $ccolor illustrations 225 1 $aARL-TR ;$v5087 300 $a"February 2010." 300 $aTitle from title screen (viewed on Mar. 31, 2011). 410 0$aARL-TR (Aberdeen Proving Ground, Md.) ;$v5087. 606 $aPolymers$xAnalysis$xComputer programs 615 0$aPolymers$xAnalysis$xComputer programs. 700 $aChantawansri$b Tanya L$01400558 701 $aLennon$b Erin M$01400559 701 $aAndzelm$b J$g(Jan)$01399374 712 02$aU.S. Army Research Laboratory. 801 0$bDTICE 801 1$bDTICE 801 2$bOCLCQ 801 2$bGPO 906 $aBOOK 912 $a9910697159303321 996 $aPredicting morphology of polymers using Mesotek+$93467693 997 $aUNINA LEADER 03843nam 2200985z- 450 001 9910557680103321 005 20220111 035 $a(CKB)5400000000044731 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76875 035 $a(oapen)doab76875 035 $a(EXLCZ)995400000000044731 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRobust Procedures for Estimating and Testing in the Framework of Divergence Measures 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (333 p.) 311 08$a3-0365-1460-0 311 08$a3-0365-1459-7 330 $aThe scope of the contributions to this book will be to present new and original research papers based on MPHIE, MHD, and MDPDE, as well as test statistics based on these estimators from a theoretical and applied point of view in different statistical problems with special emphasis on robustness. Manuscripts given solutions to different statistical problems as model selection criteria based on divergence measures or in statistics for high-dimensional data with divergence measures as loss function are considered. Reviews making emphasis in the most recent state-of-the art in relation to the solution of statistical problems base on divergence measures are also presented. 606 $aResearch & information: general$2bicssc 610 $aBayes error rate 610 $aBayesian decision making 610 $aBhattacharyya coefficient/distance 610 $abias and variance trade-off 610 $aclassification 610 $acomposite likelihood 610 $acomposite minimum density power divergence estimators 610 $aconcentration bounds 610 $acontingency tables 610 $aconvergence rates 610 $aCOVID-19 pandemic 610 $aCUSUM monitoring 610 $adensity power divergence 610 $adisparity 610 $adivergence measures 610 $aepidemiology 610 $aestimation of ? 610 $aFriedman-Rafsky test statistic 610 $aGalton-Watson branching processes with immigration 610 $aGLM model 610 $aHellinger distance 610 $aHellinger integrals 610 $aHenze-Penrose divergence 610 $aINARCH(1) model 610 $aINGARCH model 610 $ainteger-valued time series 610 $aKullback-Leibler information distance/divergence 610 $alarge deviations 610 $aMDPDE 610 $aminimal spanning trees 610 $aminimum density power divergence estimator 610 $aminimum pseudodistance estimation 610 $amixed-scale data 610 $amodel selection 610 $amonitoring 610 $an/a 610 $anumerical minimization 610 $aone-parameter exponential family 610 $apearson residuals 610 $apower divergences 610 $arare event probabilities 610 $arelative entropy 610 $aRenyi divergences 610 $aresidual adjustment function 610 $arobust change point test 610 $arobustness 610 $aRobustness 610 $aS-estimation 610 $aSPC 610 $astatistical distances 610 $atime series of counts 610 $aTukey's biweight 615 7$aResearch & information: general 700 $aPardo$b Leandro$4edt$0499080 702 $aMartin$b Nirian$4edt 702 $aPardo$b Leandro$4oth 702 $aMartin$b Nirian$4oth 906 $aBOOK 912 $a9910557680103321 996 $aRobust Procedures for Estimating and Testing in the Framework of Divergence Measures$93027216 997 $aUNINA