LEADER 02431nam0 22005413i 450 001 VAN0274507 005 20240514021845.862 017 70$2N$a9783030637576 100 $a20240408d2021 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 200 1 $aˆA ‰Course on Small Area Estimation and Mixed Models$eMethods, Theory and Applications in R$fDomingo Morales ... [et al.] 210 $aCham$cSpringer$d2021 215 $axx, 599 p.$cill.$d24 cm 410 1$1001VAN0102894$12001 $aStatistics for social and behavioral sciences$1210 $aBerlin [etc.]$cSpringer 606 $a62-XX$xStatistics [MSC 2020]$3VANC022998$2MF 606 $a62J05$xLinear regression; mixed models [MSC 2020]$3VANC023156$2MF 606 $a62P20$xApplications of statistics to economics [MSC 2020]$3VANC026444$2MF 606 $a62P25$xApplications of statistics to social sciences [MSC 2020]$3VANC031206$2MF 606 $a62D05$xSampling theory, sample surveys [MSC 2020]$3VANC037080$2MF 610 $aBest linear unbiased prediction$9KW:K 610 $aDesign-based estimation$9KW:K 610 $aEmpirical best prediction$9KW:K 610 $aEstimation of socioeconomic indicators$9KW:K 610 $aGeneralized linear mixed model$9KW:K 610 $aLabor markets surveys$9KW:K 610 $aLinear Models$9KW:K 610 $aLinear mixed models$9KW:K 610 $aLiving conditions surveys$9KW:K 610 $aMean squared error estimation$9KW:K 610 $aNested error regression models$9KW:K 610 $aPrediction Theory$9KW:K 610 $aR code$9KW:K 610 $aR packages for SAE$9KW:K 610 $aSmall area estimation$9KW:K 610 $aSurvey Methodology$9KW:K 620 $aCH$dCham$3VANL001889 702 1$aMorales$bDomingo$3VANV226937 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20240614$gRICA 856 4 $uhttps://doi.org/10.1007/978-3-030-63757-6$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN0274507 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08CONS e-book 8114 $e08eMF8114 20240412 996 $aCourse on Small Area Estimation and Mixed Models$94148952 997 $aUNICAMPANIA