Results 11 to 20 of about 617,011 (320)
Simulation comparison of modified confidence intervals based on robust estimators for coefficient of variation: skewed distributions case with real applications [PDF]
In this article, we propose confidence intervals for the population coefficient of variation based on some robust estimators such as trimmed mean, winsorized mean, Hodges-Lehmann estimator and trimean.
Hayriye E. Akyüz+2 more
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In this paper, three robust confidence intervals are proposed as alternatives to the Student t confidence interval. The performance of these intervals was compared through a simulation study shows that Qn-t confidence interval performs the best and it is as good as Student’s t confidence interval. Real-life data was used for illustration and performing
Jennifer E. V. Lloyd+8 more
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Robust inference for the unification of confidence intervals in meta-analysis [PDF]
Traditional meta-analysis assumes that the effect sizes estimated in individual studies follow a Gaussian distribution. However, this distributional assumption is not always satisfied in practice, leading to potentially biased results. In the situation when the number of studies, denoted as K, is large, the cumulative Gaussian approximation errors from
Wei Liang+3 more
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Estimators of the multiple correlation coefficient: Local robustness and confidence intervals [PDF]
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Croux, Christophe, Dehon, Catherine
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A robust and efficient algorithm to find profile likelihood confidence intervals [PDF]
AbstractProfile likelihood confidence intervals are a robust alternative to Wald’s method if the asymptotic properties of the maximum likelihood estimator are not met. However, the constrained optimization problem defining profile likelihood confidence intervals can be difficult to solve in these situations, because the likelihood function may exhibit ...
Samuel Fischer, Mark A. Lewis
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Adaptive Robust Confidence Intervals [PDF]
This paper studies the construction of adaptive confidence intervals under Huber's contamination model when the contamination proportion is unknown. For the robust confidence interval of a Gaussian mean, we show that the optimal length of an adaptive interval must be exponentially wider than that of a non-adaptive one.
Yuetian Luo, Chao Gao
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Robust Resampling Confidence Intervals for Empirical Variograms
The variogram function is an important measure of the spatial dependencies of a geostatistical or other spatial dataset. It plays a central role in kriging, designing spatial studies, and in understanding the spatial properties of geological and environmental phenomena.
Robert G. Clark, Samuel F Allingham
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On Confidence Intervals of Robust Regression Estimators
Since it is well-established that even high quality data tend to contain outliers, one would expect fat? greater reliance on robust regression techniques than is actually observed. But most of all robust regression estimators suffers from the computational difficulties and the lower efficiency than the least squares under the normal error model.
Dong‐Hee Lee+2 more
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Spatial Quantile Regression In Analysis Of Healthy Life Years In The European Union Countries [PDF]
The paper investigates the impact of the selected factors on the healthy life years of men and women in the EU countries. The multiple quantile spatial autoregression models are used in order to account for substantial differences in the healthy life ...
Grażyna Trzpiot+1 more
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Confidence Intervals Based on Robust Estimators
Meral ÇETİN, Serpil Aktaş
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