Statistical Inference for High-Dimensional Heteroscedastic Partially Single-Index Models
In this study, we propose a novel penalized empirical likelihood approach that simultaneously performs parameter estimation and variable selection in heteroscedastic partially linear single-index models with a diverging number of parameters. It is rigorously proved that the proposed method possesses...
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| Autori principali: | , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI AG
2025-09-01
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| Serie: | Entropy |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/1099-4300/27/9/964 |
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