Sentence examples for distributional confidence from inspiring English sources

DCI: distributional confidence interval.

The results for bias of estimates and of standard error are reflected in the coverage of the 95% (normal) distributional confidence interval also shown in Tables 2 and 3 with the interquartile range.

The mean bias (defined as the relative difference between true values and estimates) is obtained for all estimates The coverage of the 95% distributional confidence interval (DCI) is computed as the proportion of datasets for which the true value of the parameter was in the DCI.

Due to the uncertainty on the parametric assumption of the distributional forms, confidence intervals were calculated using a bootstrapping simulation process which is a data-based simulation method for assigning measures of accuracy to statistical estimates, used to produce inferences such as confidence intervals without knowing the type of distribution from which a sample has been taken.

In addition, distributional fairness, procedural fairness, and confidence in governance were all found to affect acceptance of mining, both directly and indirectly, by influencing the level of public trust in the mining industry.

Conventional methods for establishing confidence intervals require distributional assumptions which are not available in this case.

This approach does not require distributional assumptions (e.g., normality) and the confidence limits are not constrained to be symmetrical.

Notwithstanding this caveat, in all sampled Cape floral clades for which date estimates are available, confidence intervals for the timing of distributional and phenological shifts strongly overlap with temporal bounds of the aridification event (Table 1).

Because of the complicated distributional forms of many of these statistical parameters, 95% confidence intervals are not always readily available when using non-Bayesian statistical methods.

Bootstrap offers a robust alternative to analytic variance and confidence interval equations, because of the mild distributional assumptions, and so is often recommended in practice (e.g. Buckland et al., 2001).

Specifically, bootstrap analysis, a non-parametric approach used to avoid distributional assumptions [ 21] was used to estimate the 95% confidence interval surrounding estimated changes in average utilization and average cost in order to address the lack of normality in the distribution of the utilization and cost data.