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An efficient sampling method (based on conditioning) for tail probability estimation of this class is introduced and shown to exponentially outperform the classical Monte-Carlo estimator, in terms of coverage probability and/or confidence interval length.
Two methods were compared with respect to relative confidence interval length of parameter estimates.
Our results indicated the improvement of the WDICH method over the DICH method because for all covariates the relative confidence interval length was greater than 1.15.
The relationship between variables could then be expressed by the following equation: where z is the upper percentage point of the standard normal distribution and confidence interval length (CIL) is directly proportional to the standard deviation of RH distribution and inversely proportional to the sample size (n).
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All relative confidence interval lengths were greater than 1.15.
Despite the large family sizes in the application population, the confidence interval lengths ranged between 0.16 and 0.37.
This is consistent with results reported by Stange et al. [ 36], where the QTL confidence interval lengths decreased with an increase of marker density regardless of population size and QTL effect size.
This table depicts that all statistical parameters like mean, median, standard deviation, standard error, confidence interval, and length of confidence have better values for PSO modified by statistical tracking compared with basic and chaotic PSO.
CI, confidence interval; LOS, length of stay.
OR: odds ratio; CI: confidence interval; LOS: length of stay.
CI, confidence interval; LOS, length of hospital stay; OR, odds ratio; HR, hazards ratio.
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CEO of Professional Science Editing for Scientists @ prosciediting.com