A joint model of correlated ordinal and continuous variables
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910685604085760 |
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| author | Vana-Gür, Laura Hirk, Rainer |
| author_facet | Vana-Gür, Laura Hirk, Rainer |
| contents | In this paper we build a joint model which can accommodate for binary, ordinal and continuous responses, by assuming that the errors of the continuous variables and the errors underlying the ordinal and binary outcomes follow a multivariate normal distribution. We employ composite likelihood methods to estimate the model parameters and use composite likelihood inference for model comparison and uncertainty quantification. The complimentary R package mvordnorm implements estimation of this model using composite likelihood methods and is available for download from Github. We present two use-cases in the area of risk management to illustrate our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02924 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A joint model of correlated ordinal and continuous variables Vana-Gür, Laura Hirk, Rainer Methodology In this paper we build a joint model which can accommodate for binary, ordinal and continuous responses, by assuming that the errors of the continuous variables and the errors underlying the ordinal and binary outcomes follow a multivariate normal distribution. We employ composite likelihood methods to estimate the model parameters and use composite likelihood inference for model comparison and uncertainty quantification. The complimentary R package mvordnorm implements estimation of this model using composite likelihood methods and is available for download from Github. We present two use-cases in the area of risk management to illustrate our approach. |
| title | A joint model of correlated ordinal and continuous variables |
| topic | Methodology |
| url | https://arxiv.org/abs/2411.02924 |