03194nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001600167072002300183072001500206100007700221245010500298250001800403264006900421300006400490336002600554337002600580338003600606347002400642490004500666505039000711520115001101650001602251650004202267650001402309650003602323650009902359650001902458710003402477773002602511776003602537776003602573776003602609830004502645856004602690978-3-031-55855-9DE-He21320251030114620.0cr nn 008mamaa241216s2024    sz |    s    |||| 0|eng d  a97830315585597 a10.1007/978-3-031-55855-92doi 4aQA276-280 7aPBT2bicssc 7aMAT0290002bisacsh 7aPBT2thema1 aRudas, Tamás.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aLectures on Advanced Topics in Categorical Data Analysish[electronic resource] /cby Tamás Rudas.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXII, 377 p. 29 illus., 4 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringer Texts in Statistics,x2197-41360 a1. Introduction -- 2. Undirected graphical models -- 3. Directed graphical models -- 4. Marginal models: definition -- 5. Marginal log-linear models: applications -- 6. Path models -- 7. Relational models: definition and interpretation -- 8. Relational models as exponential families -- 9. Relational models: estimation and testing -- 10. Model testing -- 11. The mixture index of fit.  aThis book continues the mission of the previous text by the author, Lectures on Categorical Data Analysis, by expanding on the introductory concepts from that volume and providing a mathematically rigorous presentation of advanced topics and current research in statistical techniques which can be applied in the social, political, behavioral, and life sciences. It presents an intuitive and unified discussion of an array of themes in categorical data analysis, and the emphasis on structure over stochastics renders many of the methods applicable in machine learning environments and for the analysis of big data. The book focuses on graphical models, their application in causal analysis, the analytical properties of parameterizations of multivariate discrete distributions, marginal models, and coordinate-free relational models. To guide the readers in future research, the volume provides references to original papers and also offers detailed proofs of most of the significant results. Like the previous volume, it features exercises and research questions, making it appropriate for graduate students, as well as for active researchers. 0aStatistics. 0aSocial sciencesxStatistical methods. 0aBiometry.14aStatistical Theory and Methods.24aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.24aBiostatistics.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303155854208iPrinted edition:z978303155856608iPrinted edition:z9783031558573 0aSpringer Texts in Statistics,x2197-413640uhttps://doi.org/10.1007/978-3-031-55855-9