Logistic Multidimensional Data Analysis for Ordinal Response Variables using a Cumulative Link function
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866918151026900992 |
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| author | de Rooij, Mark Breemer, Ligaya Woestenburg, Dion Busing, Frank |
| author_facet | de Rooij, Mark Breemer, Ligaya Woestenburg, Dion Busing, Frank |
| contents | We present a multidimensional data analysis framework for the analysis of ordinal response variables. Underlying the ordinal variables, we assume a continuous latent variable, leading to cumulative logit models. The framework includes unsupervised methods, when no predictor variables are available, and supervised methods, when predictor variables are available. We distinguish between dominance variables and proximity variables, where dominance variables are analyzed using inner product models, whereas the proximity variables are analyzed using distance models. An expectation-majorization-minimization algorithm is derived for estimation of the parameters of the models. We illustrate our methodology with three empirical data sets highlighting the advantages of the proposed framework. A simulation study is conducted to evaluate the performance of the algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07629 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Logistic Multidimensional Data Analysis for Ordinal Response Variables using a Cumulative Link function de Rooij, Mark Breemer, Ligaya Woestenburg, Dion Busing, Frank Methodology Computation We present a multidimensional data analysis framework for the analysis of ordinal response variables. Underlying the ordinal variables, we assume a continuous latent variable, leading to cumulative logit models. The framework includes unsupervised methods, when no predictor variables are available, and supervised methods, when predictor variables are available. We distinguish between dominance variables and proximity variables, where dominance variables are analyzed using inner product models, whereas the proximity variables are analyzed using distance models. An expectation-majorization-minimization algorithm is derived for estimation of the parameters of the models. We illustrate our methodology with three empirical data sets highlighting the advantages of the proposed framework. A simulation study is conducted to evaluate the performance of the algorithm. |
| title | Logistic Multidimensional Data Analysis for Ordinal Response Variables using a Cumulative Link function |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2402.07629 |