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Variable selection for zero-inflated Poisson regression model
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DOI：10.1080/00949655.2025.2575872.png)
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Abstract
En 中文
The paper implements an efficient algorithm for variable selection in the zero-inflated Poisson regression model based on P & oacute;lya-Gamma latent variables. This leads to a closed form posterior conditional distribution under a logistic (logit) link function in modelling the excessive zeros and helps overcome the computational disadvantage of the logit link compared to a probit link. The feasibility of Gibbs sampling of the regression coefficients is particularly important in variable selection as it removes the tuning burden in the standard Metropolis-Hastings algorithm and improves the convergence. Simulation studies implement the proposed algorithm and illustrates how the choice of link functions, between the probit and the logit links, influences the variable selection and prediction results. The model comparison is also carried out in the application to a German Healthcare dataset.
Keywords:
Link functions
P & oacute
lya-Gamma distribution
spike-and-slab prior
variable selection
zero-inflated count model
Journal
J
IF:
1.2
Papers:
131
Citations:
4.1K