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Heterogeneous Overdispersed Count Data Regressions via Double Penalized Estimations

Shaomin Li, Haoyu Wei, Xiaoyu Lei

arXiv 7 Oct 2021 · Statistics — Methodology · publishedMathematics (2022) · 9 citations (OpenAlex)

arXiv:2110.03552 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper studies the non-asymptotic merits of the double $\ell_1$-regularized for heterogeneous overdispersed count data via negative binomial regressions. Under the restricted eigenvalue conditions, we prove the oracle inequalities for Lasso estimators of two partial regression coefficients for the first time, using concentration inequalities of empirical processes. Furthermore, derived from the oracle inequalities, the consistency and convergence rate for the estimators are the theoretical guarantees for further statistical inference. Finally, both simulations and a real data analysis demonstrate that the new methods are effective.

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31
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Bickel, P. J., Y. Ritov, A. B. Tsybakov, et al (2009) Simultaneous analysis of lasso and dantzig selector0.6443267%
2Zhang, H. and J. Jia (2022) Elastic-net regularized high-dimensional negative binomial regression: Consistency and weak signals detection0.6443267%
3Wang, Z., S. Ma, M. Zappitelli, C. Parikh, C.-Y. Wang, and P. Devara… (2016) Penalized count data regression with application to hospital stay after pediatric cardiac surgery0.64422100%
4Candes, E., T. Tao, et al (2007) The dantzig selector: Statistical estimation when p is much larger than n0.51121100%
5Tibshirani, R (1996) Regression shrinkage and selection via the lasso0.40511100%
6Hilbe, J. M (2011) Negative binomial regression0.40511100%
7Qiu, Y., S. X. Chen, and D. Nettleton (2018) Detecting rare and faint signals via thresholding maximum likelihood estimators0.40511100%
8Adamczak, R (2008) A tail inequality for suprema of unbounded empirical processes with applications to markov chains0.40511100%
9Cui, C., J. Jia, Y. Xiao, and H. Zhang (2021) Directional fdr control for sub-gaussian sparse glms0.40511100%
10Dai, H., Y. Bao, and M. Bao (2013) Maximum likelihood estimate for the dispersion parameter of the negative binomial distribution0.40511100%

Showing the top 10 of 31 scored citations.