LEADER 02987nam 22005295 450 001 9910438031603321 005 20220413165324.0 010 $a3-642-39912-6 024 7 $a10.1007/978-3-642-39912-1 035 $a(CKB)3710000000019131 035 $a(EBL)1466839 035 $a(OCoLC)876509035 035 $a(SSID)ssj0001010529 035 $a(PQKBManifestationID)11577426 035 $a(PQKBTitleCode)TC0001010529 035 $a(PQKBWorkID)11018142 035 $a(PQKB)10727929 035 $a(DE-He213)978-3-642-39912-1 035 $a(MiAaPQ)EBC1466839 035 $a(PPN)172428769 035 $a(EXLCZ)993710000000019131 100 $a20130907d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aUnobserved variables $emodels and misunderstandings /$fby David J. Bartholomew 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (87 p.) 225 1 $aSpringerBriefs in Statistics,$x2191-544X 300 $aDescription based upon print version of record. 311 $a3-642-39911-8 327 $a1.Unobserved Variables -- 2.Measurement, Estimation and Prediction -- 3.Simple Mixtures -- 4.Models for Ability -- 5.A General Latent Variable Model -- 6.Prediction of Latent Variables -- 7.Identifiability -- 8.Categorical Variables -- 9.Models for Time Series -- 10.Missing Data -- 11.Social Measurement -- 12.Bayesian and Computational Methods -- 13.Unity and Diversity. 330 $aThe classical statistical problem typically involves a probability distribution which depends on a number of unknown parameters. The form of the distribution may be known, partially or completely, and inferences have to be made on the basis of a sample of observations drawn from the distribution; often, but not necessarily, a random sample. This brief deals with problems where some of the sample members are either unobserved or hypothetical, the latter category being introduced as a means of better explaining the data. Sometimes we are interested in these kinds of variable themselves and sometimes in the parameters of the distribution. Many problems that can be cast into this form are treated. These include: missing data, mixtures, latent variables, time series and social measurement problems. Although all can be accommodated within a Bayesian framework, most are best treated from first principles. 410 0$aSpringerBriefs in Statistics,$x2191-544X 606 $aStatistics 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 615 0$aStatistics. 615 14$aStatistics, general. 676 $a519.5 700 $aBartholomew$b David J$4aut$4http://id.loc.gov/vocabulary/relators/aut$0102048 906 $aBOOK 912 $a9910438031603321 996 $aUnobserved Variables$92510962 997 $aUNINA