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Detection of linear substructures in calibration model by robust approach: Maximum sum of binary‐coded residuals (MASBR) regression
Author(s) -
Wang JiHong,
Jiang JianHui,
Yu RuQin
Publication year - 1996
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/(sici)1099-128x(199607)10:4<295::aid-cem423>3.0.co;2-b
Subject(s) - binary number , calibration , linear regression , matlab , set (abstract data type) , mathematics , data set , binary data , algorithm , linear model , robust regression , statistics , computer science , arithmetic , programming language , operating system
The MASBR (maximum sum of binary‐coded residuals) procedure proposed previously as a robust regression method has been modified and applied to find the linear substructures in a data set. A graphic approach is used to identify the acceptable error limit ( AEL ) in the MASBR algorithm. Some data sets with mixed linear substructures are interpreted by the proposed method showing satisfactory results. A MATLAB program for the MASBR algorithm is given.