General ways to improve false coverage rate-adjusted selective confidence intervals
| Autor: | Haibing Zhao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Applied Mathematics General Mathematics 020206 networking & telecommunications 02 engineering and technology 01 natural sciences Agricultural and Biological Sciences (miscellaneous) Confidence interval 010104 statistics & probability Statistics False coverage rate 0202 electrical engineering electronic engineering information engineering 0101 mathematics Statistics Probability and Uncertainty General Agricultural and Biological Sciences Mathematics |
| Zdroj: | Biometrika. 109:153-164 |
| ISSN: | 1464-3510 0006-3444 |
| Popis: | Summary Post-selection inference on thousands of parameters has attracted considerable research interest in recent years. Specifically, Benjamini & Yekutieli (2005) considered constructing confidence intervals after selection. They proposed adjusting the confidence levels of marginal confidence intervals for the selected parameters to ensure control of the false coverage-statement rate. However, although Benjamini–Yekutieli confidence intervals are widely used, they are uniformly inflated. In this article, two methods for narrowing the Benjamini–Yekutieli confidence intervals are proposed. The first improves the confidence intervals by incorporating the selection event into the calculation. The second method further narrows those confidence intervals in which some parameters are selected with very small probabilities, which results in underutilization of the target level for control of the false coverage-statement rate. A breast cancer dataset is analysed to compare the methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|