Results 41 to 50 of about 1,990,709 (285)
Anti-robust and tonsured statistics [PDF]
Model Assisted Statistics and Applications, 2017 This describes a statistical technique called “tonsuring” for exploratory data analysis in finance. Instead of rejecting “outlier” data that conflicts with the model, this strips out “inlier” data to get a clearer picture of how the market changes for larger moves.
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Sensors, 2020 The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research.
Divo Dharma Silalahi +4 more
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A Robust Method for Gaussian Profile Estimation in the Case of Overlapping Objects
IEEE Access, 2021 The precise stellar object identification is one of the major research fields in astronomy. In astronomical images, the 2D Gaussian function provides a good approximation of stellar objects.
Anita Gribl, Davor Petrinovic
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Frontiers in Psychology, 2013 Pearson’s correlation measures the strength of the association between two variables. The technique is, however, restricted to linear associations and is overly sensitive to outliers.
Cyril R Pernet +2 more
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Influence Analysis of Robust Wald-type Tests [PDF]
, 2015 We consider a robust version of the classical Wald test statistics for testing simple and composite null hypotheses for general parametric models. These test statistics are based on the minimum density power divergence estimators instead of the maximum ...
Ghosh, Abhik +3 more
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Statistical Procedures and Robust Statistics [PDF]
, 2002 It is argued that a main aim of statistics is to produce statistical procedures which in this article are defined as algorithms with inputs and outputs. The structure and properties of such procedures are investigated with special reference to topological and testing considerations.
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Transcriptomic pan‐cancer analysis using rank‐based Bayesian inference
Molecular Oncology, 2023 The analysis of whole genomes of pan‐cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation.
Valeria Vitelli +6 more
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Cross-validation in nonparametric regression with outliers [PDF]
, 2005 A popular data-driven method for choosing the bandwidth in standard kernel regression is cross-validation. Even when there are outliers in the data, robust kernel regression can be used to estimate the unknown regression curve [Robust and Nonlinear Time ...
Leung, Denis Heng-Yan
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The Use of Modern Robust Regression Analysis with Graphics: An Example from Marketing
StatsRoutine least squares regression analyses may sometimes miss important aspects of data. To exemplify this point we analyse a set of 1171 observations from a questionnaire intended to illuminate the relationship between customer loyalty and perceptions of
Marco Riani +3 more
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On the efficiency of Gini's mean difference [PDF]
, 2014 18 pages, 3 figures, 8 tables Acknowledgments We are indebted to Herold Dehling for introducing us to the theory of U-statistics, to Roland Fried for introducing us to robust statistics, and to Alexander Dürre, who has demonstrated the benefit of complex
Gerstenberger, Carina, Vogel, Daniel
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