Geometric weighted least squares estimation
- Optimal efficiency of least squares (LS) estimation requires that the error variables (residuals) have equal variance (homoscedasticity). In LS applications with multiple output variables, heteroscedasticity can even cause bias. In weighted LS, weights are chosen to compensate for differences in variance. The selection of these weights can be challenging, depending on the specific application. This paper introduces a general method, Geometric Weighted Least Squares (GWLS) estimation, which estimates weights using the inequality between the geometric and arithmetic means. A simulation study explores the performance of the method.
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| Author: | Reinhard OldenburgORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1181359 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/118135 |
| URL: | http://www.iapress.org/index.php/soic/article/view/2324 |
| ISSN: | 2310-5070OPAC |
| Parent Title (English): | Optimization & Information Computing |
| Publisher: | International Academic Press |
| Type: | Article |
| Language: | English |
| Year of first Publication: | 2025 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2025/01/20 |
| Volume: | 13 |
| Issue: | 2 |
| First Page: | 611 |
| Last Page: | 615 |
| DOI: | https://doi.org/10.19139/soic-2310-5070-2324 |
| Institutes: | Mathematisch-Naturwissenschaftlich-Technische Fakultät |
| Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Mathematik | |
| Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Mathematik / Lehrstuhl für Didaktik der Mathematik | |
| Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
| Licence (German): |  CC-BY 3.0: Creative Commons - Namensnennung (mit Print on Demand) |
