# Classical or 'robust' linear regression? [Klasická nebo robustní lineární regrese?]

Paper

Dohnal, Ludek and Faigl, Paul. 2006. "Classical or 'robust' linear regression? [Klasická nebo robustní lineární regrese?]." 9th Yearly Seminar on Securing Quality of Analytical Results. Komorni Lhotka, Czech Republic 27 - 29 Mar 2006 Czech Republic.
Paper/Presentation Title Classical or 'robust' linear regression? [Klasická nebo robustní lineární regrese?] Paper Dohnal, Ludek (Author) and Faigl, Paul (Author) 2006 Czech Republic https://ulbld.lf1.cuni.cz/file/213/Klasick%C3%A1%20nebo%20robustn%C3%AD%20line%C3%A1rn%C3%AD%20regrese.pdf 9th Yearly Seminar on Securing Quality of Analytical Results 9th Yearly Seminar on Securing Quality of Analytical ResultsEvent Date27 to end of 29 Mar 2006Event LocationKomorni Lhotka, Czech Republic Mathematical processing of the data in Classical Linear Regression Analysis (Least Squares Method) is compared with more 'robust' linear approaches, e.g. the Standardized Principal Components Method and the Regression Method according to Passing & Bablok (Passing-Bablok Regression, 1983). By 'robust approaches' we understand such computational methods, where there is not possible (or advantageous) to make a distinction between 'independent' and 'dependent' variables. These are fundamentally indistinguishable as a choice and their uncertainties are of a similar order of magnitude. Typically, such is the case of comparison of the data of e.g. two analytical (instrumental) procedures in chemistry. The use of the often applied Least Square Method (LSM) is in such instance inappropriate. In the LSM it is implicitly assumed that the variables have inherently different uncertainty and therefore are not mutually exchangeable.A comparison between the 3 approaches is presented in a graphical form and open for further discussion. regression analysis, least square method, analytical chemistry, biochemistry, method comparison 340199. Analytical chemistry not elsewhere classified Charles University, Czech Republic Fibre Composites Design and Development

https://research.usq.edu.au/item/9y5w9/classical-or-robust-linear-regression-klasick-nebo-robustn-line-rn-regrese

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