Results 1 to 10 of about 624,142 (260)
Introducing Robust Statistics in the Uncertainty Quantification of Nuclear Safeguards Measurements [PDF]
Entropy, 2022 The monitoring of nuclear safeguards measurements consists of verifying the coherence between the operator declarations and the corresponding inspector measurements on the same nuclear items.
Andrea Cerasa
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On Robustness for Spatio-Temporal Data
Mathematics, 2022 The spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept.
Alfonso García-Pérez
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A practical method to account for outliers in simple linear regression using the median of slopes [PDF]
Scientia Agricola, 2023 The ordinary least squares (OLS) can be affected by errors associated with heteroscedasticity and outliers, and extreme points can influence the regression parameters. Methods based on the median rather than on the mean and variance are more resistant to
Luis O. Tedeschi, Michael L. Galyean
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Ambiguity and robust statistics [PDF]
Journal of Economic Theory, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cerreia-Vioglio, Simone +3 more
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Applied Sciences, 2023 Recently the incorporation of artificial intelligence has allowed the development of valuable methodological advances in taphonomy. Some studies have achieved great precision in identifying the carnivore that produced tooth marks.
Miguel Ángel Maté-González +7 more
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Robust Regression with Density Power Divergence: Theory, Comparisons, and Data Analysis
Entropy, 2020 Minimum density power divergence estimation provides a general framework for robust statistics, depending on a parameter α , which determines the robustness properties of the method. The usual estimation method is numerical minimization of the power
Marco Riani +3 more
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Robust statistics for image deconvolution [PDF]
Astronomy and Computing, 2017 We present a blind multiframe image-deconvolution method based on robust statistics. The usual shortcomings of iterative optimization of the likelihood function are alleviated by minimizing the M-scale of the residuals, which achieves more uniform convergence across the image.
Matthias A. Lee +3 more
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Generalized resilience and robust statistics
The Annals of Statistics, 2022 Robust statistics traditionally focuses on outliers, or perturbations in total variation distance. However, a dataset could be corrupted in many other ways, such as systematic measurement errors and missing covariates. We generalize the robust statistics approach to consider perturbations under any Wasserstein distance, and show that robust estimation ...
Banghua Zhu +2 more
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Outlier Detection for Support Vector Machine using Minimum Covariance Determinant Estimator [PDF]
Journal of Artificial Intelligence and Data Mining, 2019 The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine.
M. Mohammadi, M. Sarmad
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Entropy, 2020 We review the sampling and results of the radiocarbon dating of the archaeological cloth known as the Shroud of Turin, in the light of recent statistical analyses of both published and raw data.
Paolo Di Lazzaro +5 more
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