Debiased LASSO under Poisson-Gauss Model
<|reference_start|>Debiased LASSO under Poisson-Gauss Model: Quantifying uncertainty in high-dimensional sparse linear regression is a fundamental task in statistics that arises in various applications. One of the most successful methods for quantifying uncertainty is the debiased LASSO, which has a solid theoretical foundation but is restricted to settings where the noise is purely additive. Motivated by real-world applications, we study the so-called Poisson inverse problem with additive Gaussian noise and propose a debiased LASSO algorithm that only requires $n \gg s\log^2p$ samples, which is optimal up to a logarithmic factor.<|reference_end|>
@article{abdalla2024debiased,
    title={Debiased LASSO under Poisson-Gauss Model},
    author={Pedro Abdalla and Gil Kur},
    journal={arXiv preprint arXiv:2402.16764},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16764},
    primaryClass={math.ST cs.IT eess.SP math.IT stat.TH}
}