LEADER 03444nam 22006495 450 001 9910647774803321 005 20250610121922.0 010 $a9789811994869 010 $a9811994862 024 7 $a10.1007/978-981-19-9486-9 035 $a(MiAaPQ)EBC7191413 035 $a(Au-PeEL)EBL7191413 035 $a(CKB)26089584200041 035 $a(DE-He213)978-981-19-9486-9 035 $a(PPN)268206090 035 $a(EXLCZ)9926089584200041 100 $a20230202d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMixed-Effects Models and Small Area Estimation /$fby Shonosuke Sugasawa, Tatsuya Kubokawa 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (127 pages) 225 1 $aJSS Research Series in Statistics,$x2364-0065 311 08$aPrint version: Sugasawa, Shonosuke Mixed-Effects Models and Small Area Estimation Singapore : Springer,c2023 9789811994852 320 $aIncludes bibliographical references. 327 $aIntroduction -- General Mixed-Effects Models and BLUP -- Measuring Uncertainty of Predictors -- Basic mixed-effects Models for Small Area Estimation -- Hypothesis Tests and Variable Selection -- Advanced Theory of Basic Small Area Models -- Small Area Models for Non-normal Response Variables -- Extensions of Basic Small Area Models. 330 $aThis book provides a self-contained introduction of mixed-effects models and small area estimation techniques. In particular, it focuses on both introducing classical theory and reviewing the latest methods. First, basic issues of mixed-effects models, such as parameter estimation, random effects prediction, variable selection, and asymptotic theory, are introduced. Standard mixed-effects models used in small area estimation, known as the Fay-Herriot model and the nested error regression model, are then introduced. Both frequentist and Bayesian approaches are given to compute predictors of small area parameters of interest. For measuring uncertainty of the predictors, several methods to calculate mean squared errors and confidence intervals are discussed. Various advanced approaches using mixed-effects models are introduced, from frequentist to Bayesian approaches. This book is helpful for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics. 410 0$aJSS Research Series in Statistics,$x2364-0065 606 $aStatistics 606 $aStatistics 606 $aApplied Statistics 606 $aStatistical Theory and Methods 606 $aBayesian Inference 606 $aBayesian Network 606 $aModels multinivell (Estadística)$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aStatistics. 615 14$aApplied Statistics. 615 24$aStatistical Theory and Methods. 615 24$aBayesian Inference. 615 24$aBayesian Network. 615 7$aModels multinivell (Estadística) 676 $a519.5 700 $aSugasawa$b Shonosuke$01280236 702 $aKubokawa$b Tatsuya 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910647774803321 996 $aMixed-effects models and small area estimation$93364209 997 $aUNINA