Rethinking Mean Square Error: Why Information is a Superior Assessment of Estimators
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| Publicado en: | arXiv.org (Dec 11, 2024), p. n/a |
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| Autor Principal: | |
| Publicado: |
Cornell University Library, arXiv.org
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| Materias: | |
| Acceso en liña: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3143451714 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3143451714 | ||
| 045 | 0 | |b d20241211 | |
| 100 | 1 | |a Vos, Paul | |
| 245 | 1 | |a Rethinking Mean Square Error: Why Information is a Superior Assessment of Estimators | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 11, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a James-Stein (JS) estimators have been described as showing the inadequacy of maximum likelihood estimation when assessed using mean square error (MSE). We claim the problem is not with maximum likelihood (ML) but with MSE. When MSE is replaced with a measure \(\Lambda\) of the information utilized by a statistic, likelihood based methods are superior. The information measure \(\Lambda\) describes not just point estimators but extends to Fisher's view of estimation so that we not only reconsider how estimators are assessed but also how we define an estimator. Fisher information and his views on the role of parameters, interpretation of probability, and logic of statistical inference fit well with \(\Lambda\) as measure of information. | |
| 653 | |a Statistical methods | ||
| 653 | |a Maximum likelihood estimation | ||
| 653 | |a Error analysis | ||
| 653 | |a Fisher information | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Statistical inference | ||
| 773 | 0 | |t arXiv.org |g (Dec 11, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3143451714/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.08475 |