Predicting the multivariate zero‐inflated counts: A novel model averaging method under Pearson loss
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| Format: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867020525175308289 |
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| author | Yin Liu Ziwen Gao |
| author_facet | Yin Liu Ziwen Gao Yin Liu Ziwen Gao |
| collection | Wiley Open Access |
| contents | Predicting the multivariate zero‐inflated counts: A novel model averaging method under Pearson loss Yin Liu Ziwen Gao Statistics in Medicine Excessive zeros in multivariate count data are often observed in scenarios of biomedicine and public health. To provide a better analysis on this type of data, we first develop a marginalized multivariate zero‐inflated Poisson (MZIP) regression model to directly interpret the overall exposure effects on marginal means. Then, we define a multiple Pearson residual for our newly developed MZIP regression model by simultaneously taking heterogeneity and correlation into consideration. Furthermore, a new model averaging prediction method is introduced based on the multiple Pearson residual, and the asymptotical optimality of this model averaging prediction is proved. Simulations and two empirical applications in medicine are used to illustrate the effectiveness of the proposed method. 10.1002/sim.10052 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.10052 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10052 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Predicting the multivariate zero‐inflated counts: A novel model averaging method under Pearson loss Yin Liu Ziwen Gao Statistics in Medicine Predicting the multivariate zero‐inflated counts: A novel model averaging method under Pearson loss Yin Liu Ziwen Gao Statistics in Medicine Excessive zeros in multivariate count data are often observed in scenarios of biomedicine and public health. To provide a better analysis on this type of data, we first develop a marginalized multivariate zero‐inflated Poisson (MZIP) regression model to directly interpret the overall exposure effects on marginal means. Then, we define a multiple Pearson residual for our newly developed MZIP regression model by simultaneously taking heterogeneity and correlation into consideration. Furthermore, a new model averaging prediction method is introduced based on the multiple Pearson residual, and the asymptotical optimality of this model averaging prediction is proved. Simulations and two empirical applications in medicine are used to illustrate the effectiveness of the proposed method. 10.1002/sim.10052 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Predicting the multivariate zero‐inflated counts: A novel model averaging method under Pearson loss |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10052 |