High-dimensional regression with a count response
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909314825846784 |
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| author | Zilberman, Or Abramovich, Felix |
| author_facet | Zilberman, Or Abramovich, Felix |
| contents | We consider high-dimensional regression with a count response modeled by Poisson or negative binomial generalized linear model (GLM). We propose a penalized maximum likelihood estimator with a properly chosen complexity penalty and establish its adaptive minimaxity across models of various sparsity. To make the procedure computationally feasible for high-dimensional data we consider its LASSO and SLOPE convex surrogates. Their performance is illustrated through simulated and real-data examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08821 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | High-dimensional regression with a count response Zilberman, Or Abramovich, Felix Methodology Statistics Theory We consider high-dimensional regression with a count response modeled by Poisson or negative binomial generalized linear model (GLM). We propose a penalized maximum likelihood estimator with a properly chosen complexity penalty and establish its adaptive minimaxity across models of various sparsity. To make the procedure computationally feasible for high-dimensional data we consider its LASSO and SLOPE convex surrogates. Their performance is illustrated through simulated and real-data examples. |
| title | High-dimensional regression with a count response |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2409.08821 |