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Auteur Andrew S. Fullerton |
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Ordered regression models / Andrew S. Fullerton / Boca Raton, FL ; Londres : Chapman and Hall/CRC Press (2016)
Titre : Ordered regression models : parallel, partial, and non-parallel alternatives Type de document : texte imprimé Auteurs : Andrew S. Fullerton ; Jun [USA] Xu Editeur : Boca Raton, FL ; Londres : Chapman and Hall/CRC Press Année de publication : 2016 Collection : Chapman & Hall/CRC Statistics in the social and behavioral sciences Importance : XV-171 p. Présentation : fig., tabl. Format : 27 cm ISBN/ISSN/EAN : 978-1-4665-6973-7 Note générale : Bibliogr. p. 159-165. Index Langues : Anglais (eng) Descripteurs : Logiciel statistique SAS, SPAD, SPSS, R, STATA, STATlab, TIMElab, Méthode bayésienne , Modèle de régression , Régression
STATGRAPHICS, SYSTAT, STATISTICA, BMDP...Index. décimale : 212 Modèles de régression Résumé : This book is useful to estimate and interpret results from ordered regression models. It presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. It provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption. It first introduceq the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. Then existing tests for the parallel regression assumption are reviewed and the book proposes new variations of several tests, and discusses important practical concerns related to tests of the parallel regression assumption. It also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models, and the Bayesian approach to ordered regression models. Some chapters include brief examples using Stata and R. So this book offers a conceptual framework for understanding ordered regression models based on the probability of interest and the application of the parallel regression assumption. It demonstrates the usefulness of numerous modeling alternatives, showing you how to select the most appropriate model given the type of ordinal outcome and restrictiveness of the parallel assumption for each variable. [D'après le résumé de l'éditeur] En ligne : https://www.crcpress.com/Ordered-Regression-Models-Parallel-Partial-and-Non-Para [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=119332 Ordered regression models : parallel, partial, and non-parallel alternatives [texte imprimé] / Andrew S. Fullerton ; Jun [USA] Xu . - Boca Raton, FL ; Londres : Chapman and Hall/CRC Press, 2016 . - XV-171 p. : fig., tabl. ; 27 cm. - (Chapman & Hall/CRC Statistics in the social and behavioral sciences) .
ISBN : 978-1-4665-6973-7
Bibliogr. p. 159-165. Index
Langues : Anglais (eng)
Descripteurs : Logiciel statistique SAS, SPAD, SPSS, R, STATA, STATlab, TIMElab, Méthode bayésienne , Modèle de régression , Régression
STATGRAPHICS, SYSTAT, STATISTICA, BMDP...Index. décimale : 212 Modèles de régression Résumé : This book is useful to estimate and interpret results from ordered regression models. It presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. It provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption. It first introduceq the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. Then existing tests for the parallel regression assumption are reviewed and the book proposes new variations of several tests, and discusses important practical concerns related to tests of the parallel regression assumption. It also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models, and the Bayesian approach to ordered regression models. Some chapters include brief examples using Stata and R. So this book offers a conceptual framework for understanding ordered regression models based on the probability of interest and the application of the parallel regression assumption. It demonstrates the usefulness of numerous modeling alternatives, showing you how to select the most appropriate model given the type of ordinal outcome and restrictiveness of the parallel assumption for each variable. [D'après le résumé de l'éditeur] En ligne : https://www.crcpress.com/Ordered-Regression-Models-Parallel-Partial-and-Non-Para [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=119332 Réservation
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