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We also provide a valid confidence interval called the nonparametric posterior interval for the threshold point.
We used Poisson regression with robust variances to obtain a valid confidence interval when a log-binomial analysis failed to converge [ 17].
However, the procedure by which the data are pooled must be chosen carefully if a valid confidence region is to be constructed for the combined data set.
The use of the t-distribution is known to be extremely robust and will therefore provide a valid confidence interval unless the population distribution is severely non-Normal.
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For comparison a nearest neighbor algorithm with a (only asymptotically valid) confidence interval was similarly applied.
Recent work has shown that it is possible to establish asymptotically valid confidence intervals for a quantile of the output of a model simulated using variance reduction techniques (VRTs).
It has been successfully used for various parallel machine learning projects, e.g., Capuccini et al. [21] presents an MLlib-based distributed conformal prediction implementation for valid confidence estimation for large dataset problems and shows the validity and scalability of the algorithms using two large datasets.
To obtain valid confidence intervals (CIs), we used a bootstrap procedure (Efron and Tibshirani 1993), which was conditioned on the total number of admissions (n) in the study.
Based on the characterizations of the identified sets, we provide an inference procedure that is shown to yield valid confidence sets based on inverting stochastic dominance tests.
One study [ 35] had too small a sample size (10 participants in each arm) to calculate valid confidence intervals for the risk ratio.
Our methodology is designed to be easy to implement so that researchers can produce both prevalence estimates and valid confidence intervals given their survey data in a straightforward way.
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