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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.15734 |
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| _version_ | 1866909797614354432 |
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| author | Pavithradas, Vaishnavi G, Rajesh |
| author_facet | Pavithradas, Vaishnavi G, Rajesh |
| contents | For studies in reliability, biometry, and survival analysis, the length-biased distribution is often well-suited for certain natural sampling plans. In this paper, we study the strong uniform consistency of two nonparametric estimators for the quantile-based Shannon entropy in the context of length-biased data. A simulation study is conducted to examine the behavior of the estimators in finite samples, followed by a comparative analysis with existing estimators. Furthermore, the usefulness of the proposed estimators is evaluated using a real dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15734 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Strong uniform consistency of nonparametric estimation for quantile-based entropy function under length-biased sampling Pavithradas, Vaishnavi G, Rajesh Methodology For studies in reliability, biometry, and survival analysis, the length-biased distribution is often well-suited for certain natural sampling plans. In this paper, we study the strong uniform consistency of two nonparametric estimators for the quantile-based Shannon entropy in the context of length-biased data. A simulation study is conducted to examine the behavior of the estimators in finite samples, followed by a comparative analysis with existing estimators. Furthermore, the usefulness of the proposed estimators is evaluated using a real dataset. |
| title | Strong uniform consistency of nonparametric estimation for quantile-based entropy function under length-biased sampling |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.15734 |