Ordered regression models. Parallel, partial, and non-parallel alternatives (Q2799455)
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scientific article; zbMATH DE number 6567072
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Ordered regression models. Parallel, partial, and non-parallel alternatives |
scientific article; zbMATH DE number 6567072 |
Statements
11 April 2016
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ordered regression
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ordinal dispersion
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heterogeneous choice models
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logit
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probit
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loglog models
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information measures
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Bayesian approach
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Ordered regression models. Parallel, partial, and non-parallel alternatives (English)
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This book presents a comprehensive coverage of ordered regression models divided into three classes: cumulative, stage and adjacent models. Properties of each of these classes are highlighted in applications to examples of self health evaluation, educational attainment and attitudes toward welfare spending, respectively. Parallel, partial and nonparallel formulations are separately studied. The use of logit, probit and complementary loglog link functions is compared. Relaxation of independence and homoscedasticity assumptions is considered. Tests for the parallel regression assumption are discussed, as well as the use of information measures and marginal effects to compare the relative fit of different models. A Bayesian approach is also developed. Stata and R codes are provided for different models.
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