The bias and risk functions of some Stein-rules in elliptically contoured distributions
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Bibliographic record
Abstract
In this paper, we derive the bias and risk functions of a class of shrinkage estimators of several mean parameter matrices of matrix-variate elliptically contoured distributions. More specifically, we generalize some recent findings in three ways. First, the class of distributions under consideration is more general than the Gaussian distribution case, which is often studied in literature. Second, the uncertain subspace candidate is more general than that considered in literature. Finally, we generalize some recent identities, which are useful in establishing the risk and the bias of matrix shrinkage estimators.
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Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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