Multivariate ordinal regression for multiple repeated measurements
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914662837125120 |
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| author | Vana-Gür, Laura |
| author_facet | Vana-Gür, Laura |
| contents | In this paper we propose a multivariate ordinal regression model which allows the joint modeling of three-dimensional panel data containing both repeated and multiple measurements for a collection of subjects. This is achieved by a multivariate autoregressive structure on the errors of the latent variables underlying the ordinal responses, where we distinguish between the correlations at a single point in time and the persistence over time. The error distribution is assumed to be normal or Student t distributed. The estimation is performed using composite likelihood methods. We perform several simulation exercises to investigate the quality of the estimates in different settings as well as in comparison with a Bayesian approach. The simulation study confirms that the estimation procedure is able to recover the model parameters well and is competitive in terms of computation time. We also introduce R package mvordflex and illustrate how this implementation can be used to estimate the proposed model in a user-friendly, convenient way. Finally, we illustrate the framework on a data set containing firm failure and credit ratings information from the rating agencies S&P and Moody's for US listed companies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00610 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multivariate ordinal regression for multiple repeated measurements Vana-Gür, Laura Methodology In this paper we propose a multivariate ordinal regression model which allows the joint modeling of three-dimensional panel data containing both repeated and multiple measurements for a collection of subjects. This is achieved by a multivariate autoregressive structure on the errors of the latent variables underlying the ordinal responses, where we distinguish between the correlations at a single point in time and the persistence over time. The error distribution is assumed to be normal or Student t distributed. The estimation is performed using composite likelihood methods. We perform several simulation exercises to investigate the quality of the estimates in different settings as well as in comparison with a Bayesian approach. The simulation study confirms that the estimation procedure is able to recover the model parameters well and is competitive in terms of computation time. We also introduce R package mvordflex and illustrate how this implementation can be used to estimate the proposed model in a user-friendly, convenient way. Finally, we illustrate the framework on a data set containing firm failure and credit ratings information from the rating agencies S&P and Moody's for US listed companies. |
| title | Multivariate ordinal regression for multiple repeated measurements |
| topic | Methodology |
| url | https://arxiv.org/abs/2402.00610 |