Adaptive Robust Confidence Bands on Local Polynomial Regression Using Residual Bootstrap Percentiles
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Adaptive Robust Confidence Bands on Local Polynomial Regression Using Residual Bootstrap Percentiles |
| 2. | Creator | Author's name, affiliation, country | Abil Mansyur; Universitas Negeri Medan; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Elmanani Simamora; Universitas Negeri Medan; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Muliawan Firdaus; Universitas Negeri Medan; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Tiur Malasari Siregar; Universitas Negeri Medan; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Rizki Habibi; Universitas Negeri Medan; Indonesia |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Huber weights; Local polynomial regression; Outliers; Residual bootstrap percentiles; Robust confidence bands; Tukey weights |
| 4. | Description | Abstract | Ensuring reliable inference in local polynomial regression requires robust methods that can manage data irregularities, particularly outliers. This study introduces an adaptive robust approach for constructing confidence bands using residual bootstrap percentiles. Two robust weighting techniques (Huber and Tukey) were applied to address different levels of data contamination. The method was evaluated using both simulated datasets and real-world observations involving fluctuating patterns. Huber weighting produced more stable and narrower confidence bands under moderate anomalies, while Tukey weighting was more effective in handling extreme deviations. These differences arise because Huber downweights moderate residuals proportionally, whereas Tukey aggressively suppresses extreme outliers. Smoothing parameters were optimized through cross-validation to balance bias and variance effectively. This approach enhances the robustness of nonparametric regression because it maintains consistent confidence coverage despite data imperfections, offering a reliable tool for statistical inference in complex datasets. |
| 5. | Publisher | Organizing agency, location | Universitas Pendidikan Indonesia |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2025-03-24 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ejournal.upi.edu/index.php/ijost/article/view/84323 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.17509/ijost.v10i2.84323 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Science and Technology; Vol 10, No 2 (2025): (ONLINE FIRST) IJOST: September 2025 |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2025 Universitas Pendidikan Indonesia This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |