Penalized Generative Variable Selection
<|reference_start|>Penalized Generative Variable Selection: Deep networks are increasingly applied to a wide variety of data, including data with high-dimensional predictors. In such analysis, variable selection can be needed along with estimation/model building. Many of the existing deep network studies that incorporate variable selection have been limited to methodological and numerical developments. In this study, we consider modeling/estimation using the conditional Wasserstein Generative Adversarial networks. Group Lasso penalization is applied for variable selection, which may improve model estimation/prediction, interpretability, stability, etc. Significantly advancing from the existing literature, the analysis of censored survival data is also considered. We establish the convergence rate for variable selection while considering the approximation error, and obtain a more efficient distribution estimation. Simulations and the analysis of real experimental data demonstrate satisfactory practical utility of the proposed analysis.<|reference_end|>
@article{wang2024penalized,
    title={Penalized Generative Variable Selection},
    author={Tong Wang, Jian Huang, Shuangge Ma},
    journal={arXiv preprint arXiv:2402.16661},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16661},
    primaryClass={stat.ML cs.LG stat.ME}
}