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Total Least Squares
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Geliefert zwischen Mi., 04.02.2026 und Do., 05.02.2026
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High Quality Content by WIKIPEDIA articles! Total least squares, also known as errors in variables, rigorous least squares, or orthogonal regression, is a least squares data modeling technique in which observational errors on both dependent and independent variables are taken into account. It is a generalization of Deming regression, and can be applied to both linear and non-linear models. In the least squares method of data modeling, the objective function, S, S=mathbf{r^TWr} is minimized. In linear least squares the model is defined as a linear combination of parameters, boldsymbolbeta, so the residuals are given by mathbf{r=y-Xboldsymbolbeta} There are m observations in y and n parameters in ? with mn. X is a m×n matrix whose elements are either constants or functions of the independent variables, x. The weight matrix, W, is, ideally, the inverse of the variance-covariance matrix, mathbf M_y, of the observations, y. The independent variables are assumed to be error-free. The parameter estimates are found by setting the gradient equations to zero, which results in the normal equations mathbf{X^TWXboldsymbolbeta=X^T Wy}
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786130353292
- Editor Lambert M. Surhone, Miriam T. Timpledon, Susan F. Marseken
- Sprache Englisch
- Größe H220mm x B150mm x T5mm
- Jahr 2010
- EAN 9786130353292
- Format Kartonierter Einband
- ISBN 978-613-0-35329-2
- Titel Total Least Squares
- Untertitel Deming Regression, Least Squares, Linear Least Squares, Errors and Residuals in Statistics, Covariance Matrix, Lagrange Multipliers
- Gewicht 143g
- Herausgeber VDM Verlag Dr. Müller e.K.
- Anzahl Seiten 84
- Genre Mathematik
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