Dependence-Robust Confidence Intervals for Capture–Recapture Surveys [PDF]
Journal of Survey Statistics and Methodology, 2022 Abstract Capture–recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Coronavirus Disease 2019 (COVID-19) infections, people who use drugs, sex workers, conflict casualties, and trafficking victims.
Jinghao Sun +3 more
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Robust Confidence Intervals for Effect Size in the Two-Group Case [PDF]
, 2005 The probability coverage of intervals involving robust estimates of effect size based on seven procedures was compared for asymmetrically trimming data in an independent two-groups design, and a method that symmetrically trims the data.
Algina, James +2 more
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Robust Confidence Intervals in Linear Regression [PDF]
, 2011 Sağlam regresyon yöntemlerine ilişkin çok sayıda çalışma olmasına rağmen, regresyon parametrelerinin sağlam güven aralığına ve testlerine ilişkin çalışmalar az sayıdadır. Bu çalışmaların çoğu da konum parametresinin güven aralığı üzerinedir. Bu çalışmada,
Kavruk, Tuba, Çetin, Meral
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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.
Dai, Hongsheng +3 more
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Robust Resampling Confidence Intervals for Empirical Variograms
Mathematical Geosciences, 2010 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
semanticscholar +6 more sources
ROCKET: Robust confidence intervals via Kendall’s tau for transelliptical graphical models [PDF]
Annals of Statistics, 2018 Undirected graphical models are used extensively in the biological and social sciences to encode a pattern of conditional independences between variables, where the absence of an edge between two nodes $a$ and $b$ indicates that the corresponding two ...
Rina Foygel Barber, Mladen Kolar
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Resilient Consensus for Robust Multiplex Networks with Asymmetric Confidence Intervals [PDF]
IEEE Transactions on Network Science and Engineering, 2020 The consensus problem with asymmetric confidence intervals considered in this paper is characterized by the fact that each agent can have optimistic and/or pessimistic interactions with its neighbors.
Shang, Yilun
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Bootstrap Confidence Intervals for 11 Robust Correlations in the Presence of Outliers and Leverage Observations [PDF]
Methodology, 2022 Researchers often examine whether two continuous variables (X and Y) are linearly related. Pearson’s correlation (r) is a widely-employed statistic for assessing bivariate linearity.
Johnson Ching-Hong Li
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Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs [PDF]
Econometrica, 2014 In the regression‐discontinuity (RD) design, units are assigned to treatment based on whether their value of an observed covariate exceeds a known cutoff.
Sebastian Calonico +2 more
semanticscholar +4 more sources
New robust confidence intervals for the mean under dependence [PDF]
Journal of Statistical Planning and Inference, 2020 The goal of this paper is to indicate a new method for constructing normal confidence intervals for the mean, when the data is coming from stochastic structures with possibly long memory, especially when the dependence structure is not known or even the existence of the density function.
Martial Longla, Magda Peligrad
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