Rethinking Mean Square Error: Why Information is a Superior Assessment of Estimators

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Detalles Bibliográficos
Publicado en:arXiv.org (Dec 11, 2024), p. n/a
Autor Principal: Vos, Paul
Publicado:
Cornell University Library, arXiv.org
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Acceso en liña:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
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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