Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services Data
In analyzing most correlated outcomes, the popular multivariate Gaussian distribution is very restrictive and therefore dependence modeling using copulas is nowadays very common to take into account the association among mixed outcomes. In this paper, we use Gaussian copula to construct a joint dist...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
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| Series: | Journal of Statistical Theory and Applications (JSTA) |
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| Online Access: | https://www.atlantis-press.com/article/125912322/view |
| _version_ | 1818466078937841664 |
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| author | Z. Rezaei Ghahroodi R. Aliakbari Saba T. Baghfalaki |
| author_facet | Z. Rezaei Ghahroodi R. Aliakbari Saba T. Baghfalaki |
| author_sort | Z. Rezaei Ghahroodi |
| collection | DOAJ |
| description | In analyzing most correlated outcomes, the popular multivariate Gaussian distribution is very restrictive and therefore dependence modeling using copulas is nowadays very common to take into account the association among mixed outcomes. In this paper, we use Gaussian copula to construct a joint distribution for three mixed discrete and continuous responses. Our approach entails specifying marginal regression models for the outcomes, and combining them via a copula to form a joint model. Closed form for likelihood function is obtained by considering sampling weights. We also obtain the likelihood function for mixed responses where one of the responses, time to event outcome, may have censored values. Some simulation studies are performed to illustrate the performance of the model. Finally, the model is applied on data involving trivariate mixed outcomes on hospitalization of individuals, based on the survey of household's utilization of health services. |
| first_indexed | 2024-04-13T20:53:40Z |
| format | Article |
| id | doaj.art-7eb789875e5e47fe831fedeec9634750 |
| institution | Directory Open Access Journal |
| issn | 2214-1766 |
| language | English |
| last_indexed | 2024-04-13T20:53:40Z |
| publisher | Springer |
| record_format | Article |
| series | Journal of Statistical Theory and Applications (JSTA) |
| spelling | doaj.art-7eb789875e5e47fe831fedeec96347502022-12-22T02:30:24ZengSpringerJournal of Statistical Theory and Applications (JSTA)2214-176610.2991/jsta.d.190306.009Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services DataZ. Rezaei GhahroodiR. Aliakbari SabaT. BaghfalakiIn analyzing most correlated outcomes, the popular multivariate Gaussian distribution is very restrictive and therefore dependence modeling using copulas is nowadays very common to take into account the association among mixed outcomes. In this paper, we use Gaussian copula to construct a joint distribution for three mixed discrete and continuous responses. Our approach entails specifying marginal regression models for the outcomes, and combining them via a copula to form a joint model. Closed form for likelihood function is obtained by considering sampling weights. We also obtain the likelihood function for mixed responses where one of the responses, time to event outcome, may have censored values. Some simulation studies are performed to illustrate the performance of the model. Finally, the model is applied on data involving trivariate mixed outcomes on hospitalization of individuals, based on the survey of household's utilization of health services.https://www.atlantis-press.com/article/125912322/viewCopula modelsmixed outcomessampling weightsmarginal model |
| spellingShingle | Z. Rezaei Ghahroodi R. Aliakbari Saba T. Baghfalaki Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services Data Journal of Statistical Theory and Applications (JSTA) Copula models mixed outcomes sampling weights marginal model |
| title | Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services Data |
| title_full | Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services Data |
| title_fullStr | Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services Data |
| title_full_unstemmed | Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services Data |
| title_short | Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services Data |
| title_sort | gaussian copula based regression models for the analysis of mixed outcomes an application on household s utilization of health services data |
| topic | Copula models mixed outcomes sampling weights marginal model |
| url | https://www.atlantis-press.com/article/125912322/view |
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