LEADER 01171nam0-2200337 --450 001 9910288059003321 005 20181022110854.0 010 $a978-88-6342-641-0 020 $aIT$b2014-4643 100 $a20181022d2014----kmuy0itay5050 ba 101 1 $aita 102 $aIT 105 $a 001yy 200 1 $a<>Tribunale speciale per il Libano$fMaria Stefania Cataleta$gpresentazione di Wehbé Joseph Ayach$gprefazione di Umberto Leanza$gpostfazione di Rosita Di Peri$gtraduzione di Cristiana Cavagna 210 $aNapoli$cEditoriale scientifica$d2014 215 $aXVIII, 168 p.$d21 cm 225 1 $aStudi e documenti di diritto internazionale e comunitario$v67 610 0 $aTribunale Speciale per il Libano 676 $a345.01$v23$zita 700 1$aCataleta,$bMaria Stefania$0619259 702 1$aCavagna,$bCristiana 702 1$aAyach,$bWehbé Joseph 702 1$aLeanza,$bUmberto 702 1$aDi Peri,$bRosita 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910288059003321 952 $aCOLLEZ. 1622 (67)$b2853/2018$fFSPBC 959 $aFSPBC 996 $aTribunale speciale per il Libano$91071030 997 $aUNINA LEADER 03354nam 2200781z- 450 001 9910404091103321 005 20210212 010 $a3-03928-547-5 035 $a(CKB)4100000011302227 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/61517 035 $a(oapen)doab61517 035 $a(EXLCZ)994100000011302227 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aUncertainty Quantification Techniques in Statistics 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (128 p.) 311 08$a3-03928-546-7 330 $aUncertainty quantification (UQ) is a mainstream research topic in applied mathematics and statistics. To identify UQ problems, diverse modern techniques for large and complex data analyses have been developed in applied mathematics, computer science, and statistics. This Special Issue of Mathematics (ISSN 2227-7390) includes diverse modern data analysis methods such as skew-reflected-Gompertz information quantifiers with application to sea surface temperature records, the performance of variable selection and classification via a rank-based classifier, two-stage classification with SIS using a new filter ranking method in high throughput data, an estimation of sensitive attribute applying geometric distribution under probability proportional to size sampling, combination of ensembles of regularized regression models with resampling-based lasso feature selection in high dimensional data, robust linear trend test for low-coverage next-generation sequence data controlling for covariates, and comparing groups of decision-making units in efficiency based on semiparametric regression. 610 $a?1 lasso 610 $a?2 ridge 610 $aaccuracy 610 $aadapative lasso 610 $aadaptive lasso 610 $aallele read counts 610 $aAUROC 610 $aBH-FDR 610 $adata envelopment analysis 610 $aelastic net 610 $aensembles 610 $aentropy 610 $afeature selection 610 $agene expression data 610 $agene-expression data 610 $ageometric distribution 610 $ageometric mean 610 $aGompertz distribution 610 $agroup efficiency comparison 610 $ahigh-throughput 610 $aKullback-Leibler divergence 610 $aLaplacian matrix 610 $alasso 610 $aLASSO 610 $alow-coverage 610 $aMCP 610 $amixture model 610 $anext-generation sequencing 610 $aprobability proportional to size (PPS) sampling 610 $arandomization device 610 $aresampling 610 $asandwich variance estimator 610 $aSCAD 610 $asea surface temperature 610 $asemiparametric regression 610 $asensitive attribute 610 $aSIS 610 $aSkew-Reflected-Gompertz distribution 610 $astochastic frontier model 610 $aYennum et al.'s model 700 $aKim$b Jong-Min$4auth$01326693 906 $aBOOK 912 $a9910404091103321 996 $aUncertainty Quantification Techniques in Statistics$93037675 997 $aUNINA