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Leveraged least trimmed absolute deviations [PDF]
OR Spectrum, 2021 The design of regression models that are not affected by outliers is an important task which has been subject of numerous papers within the statistics community for the last decades.
Rebennack, Steffen +1 more
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Influence of Off-Centre Positioning, Scan Direction, and Localiser Projection Angle on Organ-Specific Radiation Doses in Low-Dose Chest CT: A Simulation Study Across Four Scanner Models [PDF]
Journal of ImagingWith the considerable number of low-dose CT examinations performed in lung cancer screening, variations in participant positioning, scan direction, or localiser angle are likely to occur in practice.
Louise D’hondt +4 more
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Novel robust time series analysis for long-term and short-term prediction
Scientific Reports, 2021 Nonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers.
Hiroshi Okamura +3 more
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Hybrid Fuzzy Regression Analysis Using the F-Transform
Applied Sciences, 2020 This paper proposes a hybrid estimation algorithm for independently estimating the response function for the center and the response function for the spread in fuzzy regression model.
Hye-Young Jung +2 more
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Geometry of deviation measures for triangular distributions
Frontiers in Applied Mathematics and Statistics, 2023 Triangular distributions are widely used in many applications with limited sample data, business simulations, and project management. As with other distributions, a standard way to measure deviations is to compute the standard deviation.
Yuhe Wang, Eugene Pinsky
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Direct Least Absolute Deviation Fitting of Ellipses [PDF]
Mathematical Problems in Engineering, 2020 Scattered data from edge detection usually involve undesired noise which seriously affects the accuracy of ellipse fitting. In order to alleviate this kind of degradation, a method of direct least absolute deviation ellipse fitting by minimizing the ℓ1 algebraic distance is presented.
Gang Zhou +3 more
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Least Absolute Deviation Support Vector Regression [PDF]
Mathematical Problems in Engineering, 2014 Least squares support vector machine (LS‐SVM) is a powerful tool for pattern classification and regression estimation. However, LS‐SVM is sensitive to large noises and outliers since it employs the squared loss function. To solve the problem, in this paper, we propose an absolute deviation loss function to reduce the effects of outliers and derive a ...
Wang, Kuaini +3 more
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Constrained Least Absolute Deviation Neural Networks [PDF]
IEEE Transactions on Neural Networks, 2008 It is well known that least absolute deviation (LAD) criterion or L(1)-norm used for estimation of parameters is characterized by robustness, i.e., the estimated parameters are totally resistant (insensitive) to large changes in the sampled data. This is an extremely useful feature, especially, when the sampled data are known to be contaminated by ...
Z, Wang, B S, Peterson
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General Fitting Methods Based on Lq Norms and their Optimization
Stats, 2020 The widely used fitting method of least squares is neither unique nor does it provide the most accurate results. Other fitting methods exist which differ on the metric norm can be used for expressing the total deviations between the given data and the ...
George Livadiotis
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Analysis of least absolute deviation [PDF]
Biometrika, 2008 SUMMARY We develop a unified L 1 -based analysis-of-variance-type method for testing linear hypotheses. Like the classical L2-based analysis of variance, the method is coordinate-free in the sense that it is invariant under any linear transformation of the covariates or regression parameters.
Chen, Kani +3 more
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