Zero Inflated Bivariate Poisson Regression Models for a Sport (in)activity Data Analysis

Iannario, Maria; Ntzoufras, Ioannis; Tarantola, Claudia

doi:10.1007/978-3-031-15885-8_11

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2024

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“…Kong et al 8 extended the hurdle model, an alternative version of the ZIP model, to a multivariate case and used it to investigate crowd‐sourced bird observation data. Iannario et al 9 considered using a bivariate version of the MZIP model to examine the sport (in)activity. More applications about other MZI count models can be found in Huang et al, 10 Deng et al, 11 Lemonte, 12 Santana et al, 13 Wu et al, 14 and so on.…”

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confidence: 99%

Predicting the multivariate zero‐inflated counts: A novel model averaging method under Pearson loss

Liu,

Gao

2024

Statistics in Medicine

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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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