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- W2373426579 abstract "In linear regression analysis,the objective function of the ordinary least squares regression only takes the uncertainty of single variable into account,while the objective function in orthogonal regression or the regression based on geometric distance can overcome the ill stability incurred in the ordinary regression caused by single variable optimization.In this paper,the errors are analyzed and compared between orthogonal regression and ordinary least squares regression,which are expressed as the functions of raw data's covariances and correlation coefficients.It indicates that the error in the orthogonal regression is less than that in ordinary least squares regression.Furthermore,it is rotationally invariant.The results are proved by the 2D line fitting examples." @default.
- W2373426579 created "2016-06-24" @default.
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- W2373426579 date "2010-01-01" @default.
- W2373426579 modified "2023-09-27" @default.
- W2373426579 title "Geometric Error Analysis for Orthogonal Regression and Ordinary Least Squares Regression" @default.
- W2373426579 hasPublicationYear "2010" @default.
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