A Bootstrap Test for Symmetry based on Quantiles

Zardasht, Vali

doi:10.15672/hjms.2016.386

Cited by 1 publication

(1 citation statement)

References 14 publications

(19 reference statements)

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“…Bootstrapping has been applied widely in statistics when analytical derivations of the distribution of an estimator are intractable, and it can be found to generate distributions close to the underlying true distributions. If analytical expressions for some parameter estimators or statistical properties of these estimators are not available, estimated values of these estimators can be obtained by bootstrapping [6,29]. Variance may also be estimated with the bootstrap method [17].…”

Section: Proposed Confidence Intervalsmentioning

confidence: 99%

Robust confidence intervals for the difference of two independent population variances

Akyüz

Gamgam

2020

Hacettepe Journal of Mathematics and Statistics

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In this study, we propose confidence intervals and their bootstrap versions for the difference of variances of two independent population using some robust variance estimators. The proposed confidence intervals are compared with Herbert confidence interval in terms of coverage probability and average width. A simulation study is conducted to evaluate performances of the proposed confidence intervals under different scenarios. The simulation results indicate that the coverage probabilities for the proposed confidence intervals are very close to nominal confidence levels when the difference of population variances is zero. Confidence interval based on binary distance produces the narrowest average widths. Herbert confidence interval have not perform well for skewed distribution populations. Confidence interval based on comedian is generally recommended when the difference of population variances for skewed distributions is not zero. Average widths of bootstrap percentile confidence intervals are smaller, and decreases as sample size and nominal size increases, as expected. Consequently, we recommend bootstrap percentile confidence interval based on binary distances for skewed distributions.

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Section: Proposed Confidence Intervalsmentioning

confidence: 99%