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Ordered Regression Models parallel, partial, and non-parallel alternatives

By: Contributor(s): Publication details: Chapman & Hall: CRC Press 2016 Boca RatonDescription: xv, 165 pagesISBN:
  • 9781466569737
Subject(s): DDC classification:
  • 519.536 FUL
Summary: Estimate and Interpret Results from Ordered Regression Models Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book 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. The authors first introduce the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models,
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Item type Current library Collection Call number Status Date due Barcode
Books Books Symbiosis Institute of Business Management - Hyderabad General Text Book 519.536 FUL (Browse shelf(Opens below)) Available SIBMH-B-10690
Browsing Symbiosis Institute of Business Management - Hyderabad shelves, Shelving location: General, Collection: Text Book Close shelf browser (Hides shelf browser)
519.535 TAB Using multivariate statistics 519.535 TAB Using multivariate statistics 519.536 ARK Regression Analysis 519.536 FUL Ordered Regression Models 519.55 MAH Time Series Clustering and Classification 600 VER Material Management 600 VER Material Management


Estimate and Interpret Results from Ordered Regression Models Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book 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. The authors first introduce the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models,

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