robust statistic

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robust statistic

Robust Statistical Estimation

Robustness Analysis and Statistical Inference

Improving Statistical Efficiency and Testing Robustness of Conjoint Marginal Valuations

Length of stay as a performance indicator: robust statistical methodology

Magnetotelluric data processing with a robust statistical procedure having a high breakdown point

Statistical analysis of galaxy surveys – I. Robust error estimation for two-point clustering statistics

A robust statistical estimation of the basic parameters of single stellar populations – I. Method

Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic

The Hα surface brightness–radius relation: a robust statistical distance indicator for planetary nebulae

Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic

Statistically significant length-scale of filaments as a robust measure of galaxy distribution

A randomization-based perspective on analysis of variance: a test statistic robust to treatment effect heterogeneity

A Statistically Harmonized Alignment-Classification in Image Space Enables Accurate and Robust Alignment of Noisy Images in Single Particle Analysis

A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets

Association analysis using next-generation sequence data from publicly available control groups: the robust variance score statistic

ABACUS: an entropy-based cumulative bivariate statistic robust to rare variants and different direction of genotype effect

A statistical method for the robust detection of interannual changes in plankton abundance: analysis of monitoring data from the Bay of Fundy, Canada

Simulation Study of the Reliability and Robustness of the Statistical Methods for Detecting Positive Selection at Single Amino Acid Sites


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Statistical methods insensitive to the effects of outliers (which may be mistakes or contaminated data). The methods rely on medians rather than means, and use more information from the central than from the outlying observations. The ideas are associated with exploratory data analysis.

Subjects: Computing — Sports and Exercise Medicine.

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