MM Algorithms for Statistical Estimation in Quantile Regression
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917923966156800 |
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| author | Cheng, Yifan Kuk, Anthony Yung Cheung |
| author_facet | Cheng, Yifan Kuk, Anthony Yung Cheung |
| contents | Quantile regression \parencite{Koenker1978} is a robust and practically useful way to efficiently model quantile varying correlation and predict varied response quantiles of interest. This article constructs and tests MM algorithms, which are simple to code and have been suggested superior to some other prominent quantile regression methods in nonregularized problems \parencite{Pietrosanu2017}, in an array of linear quantile regression settings. Simulation studies comparing MM to existing tested methods and applications to various real data sets have corroborated our algorithms' effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12348 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MM Algorithms for Statistical Estimation in Quantile Regression Cheng, Yifan Kuk, Anthony Yung Cheung Methodology Applications Computation Quantile regression \parencite{Koenker1978} is a robust and practically useful way to efficiently model quantile varying correlation and predict varied response quantiles of interest. This article constructs and tests MM algorithms, which are simple to code and have been suggested superior to some other prominent quantile regression methods in nonregularized problems \parencite{Pietrosanu2017}, in an array of linear quantile regression settings. Simulation studies comparing MM to existing tested methods and applications to various real data sets have corroborated our algorithms' effectiveness. |
| title | MM Algorithms for Statistical Estimation in Quantile Regression |
| topic | Methodology Applications Computation |
| url | https://arxiv.org/abs/2407.12348 |