Algorithm 478: Solution of an Overdetermined System of Equations in the l1 Norm [F4]

Barrodale, I.; Roberts, F. D. K.

doi:10.1145/355616.361024

Cited by 298 publications

(109 citation statements)

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“…The estimating equations in (1) are solved by a modification of the Barroda and Roberts (1974) simplex linear program for any specified value of q (Koenker and Basset 1978). With little additional computation, the entire regression quantile function for all distinct values of q can be estimated (Koenker and Basset 1978;Koenker, Ng, and Portnoy 1994).…”

Section: Analytical Frameworkmentioning

confidence: 99%

Poverty in maize growing rural communities of southern Africa

Kassie¹,

Abate

Langyintuo

et al. 2014

Development Studies Research

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This study provides a detailed analysis of the extent and determinants of poverty in the maize-based rural economies of selected districts in southern African countries of Angola, Malawi, Mozambique, and Zambia. Poverty profiles showed that households that were absolutely poor had significantly smaller family sizes, fewer livestock, smaller farm sizes, smaller number of illiterate household members, and less number of important assets such as phones and radios. The study also details the extent and determinants of poverty in the study countries using quantile regression. The importance of maize technology use and resource allocation to the crop in determining the magnitude of poverty is an important finding as the sampled households were essentially semi-subsistent with limited market orientation. This finding also justifies the effort being exerted on the development and deployment of maize and maize-related technologies in rural communities of southern Africa.

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Section: Analytical Frameworkmentioning

confidence: 99%

Poverty in maize growing rural communities of southern Africa

Kassie¹,

Abate

Langyintuo

et al. 2014

Development Studies Research

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“…Iteratively reweighted LS estimation can be used to solve non-linear normal equation system of the M-estimation [18]. The M-estimation of Huber, the Mestimation of Hampel, the M-estimation of Andrews, the Danish method, the L 1 -norm and the IGGIII scheme were used [9,10,[19][20][21][22][23]. Even though the LMS method was assumed as the most robust method by [11], it was shown in [24] that this method failed with a single influential outlier.…”

Section: Outlier Conceptmentioning

confidence: 99%

“…Huber has been a pioneer [17] to robust statistics and many investigators such as [10,20] contributed to improve this statistics. They aimed to estimate unknown parameters and standard deviations discarding the disturbing effects of the outliers in such quantities.…”

Section: Outlier Conceptmentioning

confidence: 99%

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A Novel Robust Scaling for EDM Calibration Baselines using Monte Carlo Study

Erenoǧlu

2018

Teh. vjesn.

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Abstract:The terrestrial measurements using Electronic Distance Measurement (EDM) have been widely done for different applications such as deformation monitoring and establishing geodetic networks. The calibration of the EDMs reflects the quality of the estimated parameters. In geodesy, least squares principle is mainly used for estimating parameters. The least square estimation is adversely affected by the systematic and non-systematic errors resulting in bias for the estimated parameters. In this study, to compare efficacy of different robust methods, the Monte Carlo simulation is applied to the EDM calibration as well as real experiments. The parameters without errors are obtained as a result of the used methodology. The methods given in this study are basically based on iteratively reweighted least squares and can be used for both parameter estimation and outlier diagnostics. This is of particular importance for calibrations of electromagnetic distance measurements using the Monte Carlo simulation and the measured test baselines. The results showed that one of the advantages of the used methodology is the improvement of the reliability of the estimated calibration parameters.

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“…that only few of its entries are different zero, one may search for that vector ζ that maximizes the number of zero entries in y. We may achieve this by solving the optimization problem [4,34,35,64] arg min 6) employing the Barrowdale and Roberts algorithm [120].…”

Section: Appendix B Singular Value Decomposition and L 1 -Norm Minimimentioning

confidence: 99%