LEADER 04367nam 22006855 450 001 9910734835003321 005 20240619143439.0 010 $a3-031-31186-8 024 7 $a10.1007/978-3-031-31186-4 035 $a(MiAaPQ)EBC30622141 035 $a(Au-PeEL)EBL30622141 035 $a(DE-He213)978-3-031-31186-4 035 $a(PPN)272250775 035 $a(CKB)27532072700041 035 $a(EXLCZ)9927532072700041 100 $a20230708d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTrends and Challenges in Categorical Data Analysis $eStatistical Modelling and Interpretation /$fedited by Maria Kateri, Irini Moustaki 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (323 pages) 225 1 $aStatistics for Social and Behavioral Sciences,$x2199-7365 311 08$aPrint version: Kateri, Maria Trends and Challenges in Categorical Data Analysis Cham : Springer International Publishing AG,c2023 9783031311857 327 $aPreface -- Chapter 1. Carolyn J. Anderson, Maria Kateri and Irini Moustaki: Log-Linear and Log-Multiplicative Association Models for Categorical Data -- Chapter 2. Peter W. F. Smith: Graphical Models for Categorical Data -- Chapter 3. Tam´as Rudas and Wicher Bergsma: Marginal Models: an Overview -- Chapter 4. Jonathan J Forster and Mark E Grigsby: Bayesian Inference for Multivariate Categorical Data -- Chapter 5. Alan Agresti, Claudia Tarantola and Roberta Varriale: Simple Ways to Interpret Effects in Modeling Binary Data -- Chapter 6. Ioannis Kosmidis: Mean and median bias reduction: A concise review and application to adjacent-categories logit models -- Chapter 7. Jan Gertheiss and Gerhard Tutz: Regularization and Predictor Selection for Ordinal and Categorical Data -- Chapter 8. Mirko Armillotta, Alessandra Luati and Monia Lupparelli: An overview of ARMA-like models for count and binary data -- Chapter 9. Francesco Valentini, Claudia Pigini, and Francesco Bartolucci: Advances in maximum likelihood estimation of fixed-effects binary panel data models. 330 $aThis book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences. 410 0$aStatistics for Social and Behavioral Sciences,$x2199-7365 606 $aStatistics 606 $aPsychometrics 606 $aEpidemiology 606 $aStatistical Theory and Methods 606 $aStatistics in Life Sciences, Medicine, Health Sciences 606 $aPsychometrics 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aEpidemiology 606 $aAnàlisi multivariable$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aPsychometrics. 615 0$aEpidemiology. 615 14$aStatistical Theory and Methods. 615 24$aStatistics in Life Sciences, Medicine, Health Sciences. 615 24$aPsychometrics. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aEpidemiology. 615 7$aAnàlisi multivariable 676 $a519.535 676 $a519.535 700 $aKateri$b Maria$0721771 701 $aMoustaki$b Irini$0522145 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734835003321 996 $aTrends and Challenges in Categorical Data Analysis$93404481 997 $aUNINA