001471732 000__ 04311cam\\2200589\i\4500
001471732 001__ 1471732
001471732 003__ OCoLC
001471732 005__ 20230908003312.0
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001471732 007__ cr\cn\nnnunnun
001471732 008__ 230714s2023\\\\sz\a\\\\o\\\\\000\0\eng\d
001471732 020__ $$a9783031311864$$q(electronic bk.)
001471732 020__ $$a3031311868$$q(electronic bk.)
001471732 020__ $$z9783031311857
001471732 020__ $$z303131185X
001471732 0247_ $$a10.1007/978-3-031-31186-4$$2doi
001471732 035__ $$aSP(OCoLC)1390444731
001471732 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCQ
001471732 049__ $$aISEA
001471732 050_4 $$aQA278
001471732 08204 $$a519.5/35$$223/eng/20230714
001471732 24500 $$aTrends and challenges in categorical data analysis :$$bstatistical modelling and interpretation /$$cedited by Maria Kateri, Irini Moustaki.
001471732 264_1 $$aCham :$$bSpringer,$$c2023.
001471732 300__ $$a1 online resource (340 pages) :$$billustrations (black and white, and colour).
001471732 336__ $$atext$$btxt$$2rdacontent
001471732 337__ $$acomputer$$bc$$2rdamedia
001471732 338__ $$aonline resource$$bcr$$2rdacarrier
001471732 4901_ $$aStatistics for social and behavioral sciences
001471732 5050_ $$aPreface -- Chapter 1. Carolyn J. Anderson, Maria Kateri and Irini Moustaki: Log-Linear and Log-Multiplicative Association Models for Categorical Data -- Chapter 2. Peter W. F. Smith: Graphical Models for Categorical Data -- Chapter 3. Tamas Rudas and Wicher Bergsma: Marginal Models: an Overview -- Chapter 4. Jonathan J Forster and Mark E Grigsby: Bayesian Inference for Multivariate Categorical Data -- Chapter 5. Alan Agresti, Claudia Tarantola and Roberta Varriale: Simple Ways to Interpret Effects in Modeling Binary Data -- Chapter 6. Ioannis Kosmidis: Mean and median bias reduction: A concise review and application to adjacent-categories logit models -- Chapter 7. Jan Gertheiss and Gerhard Tutz: Regularization and Predictor Selection for Ordinal and Categorical Data -- Chapter 8. Mirko Armillotta, Alessandra Luati and Monia Lupparelli: An overview of ARMA-like models for count and binary data -- Chapter 9. Francesco Valentini, Claudia Pigini, and Francesco Bartolucci: Advances in maximum likelihood estimation of fixed-effects binary panel data models.
001471732 506__ $$aAccess limited to authorized users.
001471732 520__ $$aThis book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences.
001471732 588__ $$aDescription based on print version record.
001471732 650_0 $$aMultivariate analysis.
001471732 655_0 $$aElectronic books.
001471732 7001_ $$aKateri, Maria,$$eeditor.
001471732 7001_ $$aMoustaki, Irini,$$eeditor.$$1https://isni.org/isni/0000000124112769
001471732 77608 $$iPrint version:$$tTrends and challenges in categorical data analysis.$$dCham : Springer, 2023$$z9783031311857$$w(OCoLC)1381122409
001471732 830_0 $$aStatistics for social and behavioral sciences.
001471732 852__ $$bebk
001471732 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-31186-4$$zOnline Access$$91397441.1
001471732 909CO $$ooai:library.usi.edu:1471732$$pGLOBAL_SET
001471732 980__ $$aBIB
001471732 980__ $$aEBOOK
001471732 982__ $$aEbook
001471732 983__ $$aOnline
001471732 994__ $$a92$$bISE