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In statistical estimation theory, the coverage probability, or coverage for short, is the probability that a confidence interval or confidence region will include the true value (parameter) of interest. It can be defined as the proportion of instances where the interval surrounds the true value as assessed by long-run frequency.
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probability coverage interval confidence nominal value true include intervals actual equal parameter proportion distribution construction estimation statistical defined instances surrounds
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Coverage probability | is a | probability that a prediction interval will include an out-of-sample value of the random variable | 0.90 | text |
| Coverage probability | is a | actual probability that the interval contains the parameter.If all assumptions used in deriving a confidence interval are met | 0.90 | text |
| Coverage probability | is a | fraction of these computed confidence intervals that include the desired but unobservable parameter value | 0.90 | text |
| Coverage probability | related to Concept | The | 0.60 | section |
| Coverage probability | related to Concept | Hence | 0.60 | section |
| Coverage probability | related to Concept | By | 0.60 | section |
| Coverage probability | related to Concept | If | 0.60 | section |
| Coverage probability | related to Concept | When | 0.60 | section |
| Coverage probability | related to Concept | For | 0.60 | section |
| Coverage probability | related to Concept | In | 0.60 | section |
| Coverage probability | related to Probability Matching | In | 0.60 | section |
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