A Tutorial on Asymptotic Properties for Biostatisticians with Applications to COVID-19 Data
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866912026033389568 |
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| author | Cui, Elvis Han |
| author_facet | Cui, Elvis Han |
| contents | Asymptotic properties of statistical estimators play a significant role both in practice and in theory. However, many asymptotic results in statistics rely heavily on the independent and identically distributed (iid) assumption, which is not realistic when we have fixed designs. In this article, we build a roadmap of general procedures for deriving asymptotic properties under fixed designs and the observations need not to be iid. We further provide their applications in many statistical applications. Finally, we apply our results to Poisson regression using a COVID-19 dataset as an illustration to demonstrate the power of these results in practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_07351 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Tutorial on Asymptotic Properties for Biostatisticians with Applications to COVID-19 Data Cui, Elvis Han Methodology Artificial Intelligence Statistics Theory Applications Asymptotic properties of statistical estimators play a significant role both in practice and in theory. However, many asymptotic results in statistics rely heavily on the independent and identically distributed (iid) assumption, which is not realistic when we have fixed designs. In this article, we build a roadmap of general procedures for deriving asymptotic properties under fixed designs and the observations need not to be iid. We further provide their applications in many statistical applications. Finally, we apply our results to Poisson regression using a COVID-19 dataset as an illustration to demonstrate the power of these results in practice. |
| title | A Tutorial on Asymptotic Properties for Biostatisticians with Applications to COVID-19 Data |
| topic | Methodology Artificial Intelligence Statistics Theory Applications |
| url | https://arxiv.org/abs/2211.07351 |