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Least orthogonal absolute deviations
Computational Statistics & Data Analysis, 1988zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Exact Computation of Censored Least Absolute Deviations Estimators
SSRN Electronic Journal, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bilias, Yannis +2 more
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Least absolute deviation (LAD) image matching
ISPRS Journal of Photogrammetry and Remote Sensing, 1996Abstract The robust estimator properties of the L,-norm or least absolute deviation (LAD) is shown to provide better subpixel matching accuracy in the presence of outlier points than the least squares method widely employed for image matching applications.
M.F. Calitz, H. Rüther
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Distributed Least Absolute Deviations Estimation
Journal of Guidance, Control, and DynamicsDistributed algorithms are essential for reducing communication costs, computational complexity, and memory requirements while performing collaborative estimation using multi-agent systems. Additionally, robustness in estimators is important to prevent performance degradation when the measurement noise is non-Gaussian.
Kaushik Prabhu +3 more
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A Joint Least Squares and Least Absolute Deviation Model
IEEE Signal Processing Letters, 2019We propose a joint least squares and least absolute deviations (JOLESALAD) model, show that the proposed model can cover least absolute shrinkage and selection operator (LASSO) and two of its variants, namely the generalized LASSO (gLASSO) and the constrained LASSO (cLASSO), and prove that under a full rank condition, the JOLESALAD can be transformed ...
Junbo Duan +3 more
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Robust object tracking using least absolute deviation
Image and Vision Computing, 2014Recently, sparse representation has been applied to object tracking, where each candidate target is approximately represented as a sparse linear combination of target templates. In this paper, we present a new tracking algorithm, which is faster and more robust than other tracking algorithms, based on sparse representation.
Jingyu Yan +3 more
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Sparse Least Absolute Deviation Support Vector Machine
The Korean Data Analysis Society, 2023The support vector machine solves a quadratic programming problem with linear inequality and equality constraints. However, it is not trivial to solve the quadratic problem. The least squares support vector machine(LS-SVM) solves a linear system by equality constraints instead of inequality constraints.
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LEAST ABSOLUTE DEVIATIONS REGRESSION UNDER NONSTANDARD CONDITIONS
Econometric Theory, 2001Most work on the asymptotic properties of least absolute deviations (LAD) estimators makes use of the assumption that the common distribution of the disturbances has a density that is both positive and finite at zero. We consider the implications of weakening this assumption in a number of regression settings, primarily with a time series ...
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Fuzzy regression using least absolute deviation estimators
Soft Computing, 2007In fuzzy regression, that was first proposed by Tanaka et al. (Eur J Oper Res 40:389–396, 1989; Int Cong Appl Syst Cybern 4:2933–2938, 1980; IEEE Trans SystMan Cybern 12:903–907, 1982), there is a tendency that the greater the values of independent variables, the wider the width of the estimated dependent variables.
Seung Hoe Choi, James J. Buckley
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Multi-object tracking using least absolute deviation
2014 7th International Congress on Image and Signal Processing, 2014Recently, attention has been paid to tracking methods using sparse representation. Assuming that the representation residuals follow Gaussian distribution, the multi-object tracking methods based on sparse representation are proposed. However, these methods are sensitive to outliers such as occlusion due to the assumption of Gaussian distribution.
Bing Wang, Fuxiang Wang
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