Confidence intervals for intentionally biased estimators
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916593583259648 |
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| author | Kaplan, David M. Liu, Xin |
| author_facet | Kaplan, David M. Liu, Xin |
| contents | We propose and study three confidence intervals (CIs) centered at an estimator that is intentionally biased to reduce mean squared error. The first CI simply uses an unbiased estimator's standard error; compared to centering at the unbiased estimator, this CI has higher coverage probability for confidence levels above 91.7%, even if the biased and unbiased estimators have equal mean squared error. The second CI trades some of this "excess" coverage for shorter length. The third CI is centered at a convex combination of the two estimators to further reduce length. Practically, these CIs apply broadly and are simple to compute. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Confidence intervals for intentionally biased estimators Kaplan, David M. Liu, Xin Econometrics Statistics Theory Methodology 62F25, 62G15 We propose and study three confidence intervals (CIs) centered at an estimator that is intentionally biased to reduce mean squared error. The first CI simply uses an unbiased estimator's standard error; compared to centering at the unbiased estimator, this CI has higher coverage probability for confidence levels above 91.7%, even if the biased and unbiased estimators have equal mean squared error. The second CI trades some of this "excess" coverage for shorter length. The third CI is centered at a convex combination of the two estimators to further reduce length. Practically, these CIs apply broadly and are simple to compute. |
| title | Confidence intervals for intentionally biased estimators |
| topic | Econometrics Statistics Theory Methodology 62F25, 62G15 |
| url | https://arxiv.org/abs/2502.00450 |