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  • The Total Least Squares is one of the most widely use method for data analysis, where both dependent and independent variables are observed with random errors. If data set contains outliers, the robustified version of TLS such as mixed Least Trimmed Squares - Total Least Trimmed Squares method is used. The disadvantage of this method is absence of exact algorithm that can find solution of the estimation in real time for data sets with large number of observations. In this paper we introduced the BSA algorithm for LTS-TLTS and compare it with BAB algorithm. It is the first introduction to this method for such a problem and only first results from simulation study are shown.
  • The Total Least Squares is one of the most widely use method for data analysis, where both dependent and independent variables are observed with random errors. If data set contains outliers, the robustified version of TLS such as mixed Least Trimmed Squares - Total Least Trimmed Squares method is used. The disadvantage of this method is absence of exact algorithm that can find solution of the estimation in real time for data sets with large number of observations. In this paper we introduced the BSA algorithm for LTS-TLTS and compare it with BAB algorithm. It is the first introduction to this method for such a problem and only first results from simulation study are shown. (en)
Title
  • Borders Scanning Algorithm to Solving Total Least Trimmed Squares Estimation
  • Borders Scanning Algorithm to Solving Total Least Trimmed Squares Estimation (en)
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  • Borders Scanning Algorithm to Solving Total Least Trimmed Squares Estimation
  • Borders Scanning Algorithm to Solving Total Least Trimmed Squares Estimation (en)
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  • RIV/68407700:21340/12:00199818!RIV14-MSM-21340___
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  • RIV/68407700:21340/12:00199818
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  • robust regression analysis; error in variables model; robustified total least squares; total least trimmed squares; BSA - borders scanning algorithm; branch-and-bound algorithm (en)
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  • Franc, Jiří
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  • 21340
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