LEADER 03528nam 22006255 450 001 9910917791903321 005 20250527184123.0 010 $a9783031558559 010 $a3031558553 024 7 $a10.1007/978-3-031-55855-9 035 $a(CKB)37037245900041 035 $a(MiAaPQ)EBC31849269 035 $a(Au-PeEL)EBL31849269 035 $a(DE-He213)978-3-031-55855-9 035 $a(EXLCZ)9937037245900041 100 $a20241216d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLectures on Advanced Topics in Categorical Data Analysis /$fby Tamás Rudas 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (385 pages) 225 1 $aSpringer Texts in Statistics,$x2197-4136 311 08$a9783031558542 311 08$a3031558545 327 $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. 330 $aThis book continues the mission of the previous text by the author, Lectures on Categorical Data Analysis, by expanding on the introductory concepts from that volume and providing a mathematically rigorous presentation of advanced topics and current research in statistical techniques which can be applied in the social, political, behavioral, and life sciences. It presents an intuitive and unified discussion of an array of themes in categorical data analysis, and the emphasis on structure over stochastics renders many of the methods applicable in machine learning environments and for the analysis of big data. The book focuses on graphical models, their application in causal analysis, the analytical properties of parameterizations of multivariate discrete distributions, marginal models, and coordinate-free relational models. To guide the readers in future research, the volume provides references to original papers and also offers detailed proofs of most of the significant results. Like the previous volume, it features exercises and research questions, making it appropriate for graduate students, as well as for active researchers. 410 0$aSpringer Texts in Statistics,$x2197-4136 606 $aStatistics 606 $aSocial sciences$xStatistical methods 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 606 $aBiostatistics 606 $aBiometria$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aSocial sciences$xStatistical methods. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 615 24$aBiostatistics. 615 7$aBiometria 676 $a001.422 700 $aRudas$b Tama?s$00 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910917791903321 996 $aLectures on Advanced Topics in Categorical Data Analysis$94303742 997 $aUNINA