Results 11 to 20 of about 782,515 (314)
Robust Method for Confidence Interval Estimation in Outlier-Prone Datasets: Application to Molecular and Biophysical Data. [PDF]
BiomoleculesEstimating confidence intervals in small or noisy datasets is a recurring challenge in biomolecular research, particularly when data contain outliers or exhibit high variability.
Golovko VV.
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A robust and efficient algorithm to find profile likelihood confidence intervals [PDF]
Statistics and Computing, 2021 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 ...
Mark A. Lewis +2 more
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Adaptive Robust Confidence Intervals [PDF]
arXivThis 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.
Luo, Yuetian, Gao, Chao
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Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina. [PDF]
Sensors (Basel), 2020 In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to 2.5 μ m ) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built.
Hernandez W +3 more
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Spatial Quantile Regression In Analysis Of Healthy Life Years In The European Union Countries [PDF]
Comparative Economic Research, 2016 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 ...
Orwat-Acedańska, Agnieszka +1 more
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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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CESER: An R Package to Compute Cluster Estimated Standard Errors
Journal of Open Research Software, 2021 This paper presents an implementation in R of the Cluster Estimated Standard Errors (CESE) proposed by [12]. The method estimates the covariance matrix of the estimated coefficients of linear models in grouped data sets with correlation among ...
Diogo Ferrari
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Confidence interval methods for antimicrobial resistance surveillance data
Antimicrobial Resistance and Infection Control, 2021 Background Antimicrobial resistance (AMR) is one of the greatest global health challenges today, but burden assessment is hindered by uncertainty of AMR prevalence estimates.
Erta Kalanxhi +3 more
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Sensors, 2021 This paper analyzes 12 years of tropospheric ozone (O3) concentration measurements using robust techniques. The measurements were taken at an air quality monitoring station called Belisario, which is in Quito, Ecuador; the data collection time period was
Wilmar Hernandez +4 more
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Contrasting diversity values: statistical inferences based on overlapping confidence intervals. [PDF]
PLoS ONE, 2013 Ecologists often contrast diversity (species richness and abundances) using tests for comparing means or indices. However, many popular software applications do not support performing standard inferential statistics for estimates of species richness and ...
Ian MacGregor-Fors, Mark E Payton
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