Abstract
An attractive alternative to least‐squares data modeling techniques is the use of absolute value error criteria. Unlike the least‐squares techniques the inclusion of some infinite blunders along with the data will hardly affect the solution to an otherwise well‐posed problem. An example of this great stability is seen when an average is, determined by using the median rather than the arithmetic mean. Algorithms for absolute error minimization are often approximately as costly as least‐squares algorithms; however, unlike least‐squares, they naturally lend themselves to inequality or bounding constraints on models.
Keywords
Least-squares function approximationLeast absolute deviationsBounding overwatchTotal least squaresMinificationMathematicsStability (learning theory)AlgorithmComputer scienceIteratively reweighted least squaresNon-linear least squaresMathematical optimizationExplained sum of squaresStatisticsRegressionArtificial intelligenceMachine learningSingular value decomposition
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Publication Info
- Year
- 1973
- Type
- article
- Volume
- 38
- Issue
- 5
- Pages
- 826-844
- Citations
- 807
- Access
- Closed
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Cite This
Jon F. Claerbout,
Francis Muir
(1973).
ROBUST MODELING WITH ERRATIC DATA.
Geophysics
, 38
(5)
, 826-844.
https://doi.org/10.1190/1.1440378
Identifiers
- DOI
- 10.1190/1.1440378