LEADER 00843nam0-22002891i-450- 001 990006152680403321 005 19980601 035 $a000615268 035 $aFED01000615268 035 $a(Aleph)000615268FED01 035 $a000615268 100 $a19980601d1956----km-y0itay50------ba 105 $a--------00-yy 200 1 $a<>Reichshaftpflichtgesetz$ferlantert von dr. Franz Biermann. 210 $aMunster Westf$cAschendorffsche Verlagsbuchhandlung$d1956 215 $a27 p.$d20 cm 225 1 $aAschendorffs juristische Handbücherei$v22 676 $a342.088 700 1$aBiermann,$bFranz$0233560 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006152680403321 952 $aCOLL. 305 (22)$b75632$fFGBC 959 $aFGBC 996 $aReichshaftpflichtgesetz$9645265 997 $aUNINA DB $aGIU01 LEADER 03352oam 2200661I 450 001 9910416126603321 005 20180731045331.0 010 $a1-000-21873-2 010 $a0-429-09620-8 010 $a1-4665-0753-5 024 7 $a10.1201/b13902 035 $a(CKB)2670000000333574 035 $a(EBL)1128533 035 $a(OCoLC)829461089 035 $a(SSID)ssj0000832626 035 $a(PQKBManifestationID)11442998 035 $a(PQKBTitleCode)TC0000832626 035 $a(PQKBWorkID)10899299 035 $a(PQKB)10567408 035 $a(MiAaPQ)EBC1128533 035 $a(CaSebORM)9781466507531 035 $a(OCoLC)827944838 035 $a(EXLCZ)992670000000333574 100 $a20180331d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAge-period-cohort analysis $enew models, methods, and empirical applications /$fYang Yang and Kenneth C. Land 205 $a1st edition 210 1$aBoca Raton, Fla. :$cCRC Press,$d2013. 215 $a1 online resource (339 p.) 225 1 $aChapman & Hall/CRC interdisciplinary statistics series 300 $aA Chapman & Hall book. 311 $a1-322-66825-6 311 $a1-4665-0752-7 320 $aIncludes bibliographical references and index. 327 $a1. Introduction -- 2. Why cohort analysis? -- 3. APC analysis of data from three common research designs -- 4. Formalities of the age-period-cohort analysis conundrum and a generalized linear mixed models (GLMM) framework -- 5. APC accounting/multiple classification model, part I : model identification and estimation using the intrinsic estimator -- 6. APC accounting/multiple classification model, part II : empirical applications -- 7. Mixed effects models : hierarchical APC-cross-classified random effects models (HAPC-CCREM), part I : the basics -- 8. Mixed effects models : hierarchical APC-cross-classified random effects models (HAPC-CCREM), part II : advanced analyses -- 9. Mixed effects models : hierarchical APC-growth curve analysis of prospective cohort data -- 10. Directions for future research and conclusion. 330 $aAge-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors' collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables d 410 0$aInterdisciplinary statistics. 606 $aCohort analysis 606 $aAge groups$xStatistical methods 608 $aElectronic books. 615 0$aCohort analysis. 615 0$aAge groups$xStatistical methods. 676 $a001.42/2 676 $a001.422 676 $a300.727 700 $aYang$b Yang$f1975,$0891136 701 $aLand$b Kenneth C$034144 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910416126603321 996 $aAge-period-cohort analysis$91990444 997 $aUNINA