Contents
When does coverage probability equal the confidence interval?
If all assumptions used in deriving a confidence interval are met, the nominal coverage probability will equal the coverage probability (termed “true” or “actual” coverage probability for emphasis). If any assumptions are not met, the actual coverage probability could either be less than or greater than the nominal coverage probability.
Which is the maximum likelihood estimate based on rejection rate?
Consider now the asymptotic performance (as measured by rejection rates) for the maximum likelihood estimate based on score functions which are not necessarily correct, that is, when possibly φ i ≠ φ i ⋆. We can use the results of section 4.4.5 which become more tractable in the iid case.
What happens to coverage probability if assumptions are not met?
If any assumptions are not met, the actual coverage probability could either be less than or greater than the nominal coverage probability.
Why are rejection rates low in the Rec?
Moreover, rejection rates may be a poor indicator of the REC’s quality; protocols may be improved in anticipation of the REC’s requirements and investigators, fearing rejection, may decide not to submit proposals they think might be rejected by the committee.
What is the purpose of a confidence interval?
The confidence interval aims to contain the unknown mean remission duration with a given probability. This is the “confidence level” or “confidence coefficient” of the constructed interval which is effectively the “nominal coverage probability” of the procedure for constructing confidence intervals.
How is the confidence interval computed in Neyman construction?
In these hypothetical repetitions, independent data sets following the same probability distribution as the actual data are considered, and a confidence interval is computed from each of these data sets; see Neyman construction.
What is the empirical coverage probability of the CI?
This graph shows why the term “coverage probability” is used: it is the probability that one of the vertical lines in the graph will “cover” the population mean. The previous simulation confirms that the empirical coverage probability of the CI is 95% for normally distributed data.