A Commentary on Chatterjee Et Al. (2018): A Corrected Framework for Group Sparsity in Zero-Inflated Negative Binomial Models.

We reexamine GOOOGLE, the group-regularized zero-inflated negative binomial (ZINB) approach of Chatterjee et al. We show that in the released implementation, the tuning parameter is selected using a Bayesian information criterion (BIC) computed on a Gaussian surrogate. Because the unpenalized model fits this surrogate exactly (with zero residual sum of squares), the BIC always favors essentially unpenalized solutions and fails to induce group sparsity. This results in zero group specificity in simulations mirroring the original paper's design. We demonstrate that this issue is resolved by selecting the tuning parameter using the true ZINB log-likelihood. Furthermore, we propose the fully iterative group broken adaptive ridge (grBAR) estimator as a more robust alternative. Our open-source R package, group regularization algorithms for zero-inflated models (GRAZIMs), provides these tools to enable reliable group selection in ZINB models.