Predicting the multivariate zero-inflated counts: A novel model averaging method under Pearson loss.
Stat Med
; 43(11): 2096-2121, 2024 May 20.
Article
em En
| MEDLINE
| ID: mdl-38488240
ABSTRACT
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.
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Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
Modelos Estatísticos
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article