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  • Classical regression estimators, such as the Ordinary Least Squares, are very sensitive to occurrence of outliers and they are not consistent when orthogonality condition is broken and both independent and some dependent variables are considered to be measured with a random error. The method which can cope with this problem is robustified version of the mixed Least Squares - Total Least Squares method, based on the idea of down-weighting the influential points. The existence and the uniqueness of the solution is discussed and di(R)erent approaches of calculation are described. The computational complexity is shown and the exact algorithm based on a branch-and-bound technique that guarantees global optimality is presented. The implementation of the algorithms and theirs application to simulated and real data sets are shown.
  • Classical regression estimators, such as the Ordinary Least Squares, are very sensitive to occurrence of outliers and they are not consistent when orthogonality condition is broken and both independent and some dependent variables are considered to be measured with a random error. The method which can cope with this problem is robustified version of the mixed Least Squares - Total Least Squares method, based on the idea of down-weighting the influential points. The existence and the uniqueness of the solution is discussed and di(R)erent approaches of calculation are described. The computational complexity is shown and the exact algorithm based on a branch-and-bound technique that guarantees global optimality is presented. The implementation of the algorithms and theirs application to simulated and real data sets are shown. (en)
Title
  • Some computational aspects of robustifed total least squares
  • Some computational aspects of robustifed total least squares (en)
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  • Some computational aspects of robustifed total least squares
  • Some computational aspects of robustifed total least squares (en)
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  • RIV/68407700:21340/11:00189482!RIV12-MSM-21340___
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  • RIV/68407700:21340/11:00189482
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  • regression coeficients, least square method (en)
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  • SPMS 2011 Stochastic and Physical Monitoring Systems - Proceedings
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  • Franc, Jiří
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  • České vysoké učení technické v Praze. Fakulta jaderná a fyzikálně inženýrská
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  • 978-80-01-04916-7
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  • 21340
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