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摘要
En 中文
This paper deals with the problem of the regression of measured quantities when measurement uncertainty affects both the regressed quantity Find the independent variables. A new criterion is given, named the full least-squares method. A compact matrix notation is used for deriving the parameter vector of the regression model and its uncertainty variance-covariance matrix. Some examples of application of the novel method are given together with a comparison with the regression obtained by the generalized least-squares and the total least-squares methods.
Keyword:
calibration
curve fitting
data models
least-squares
parameter estimation
parameter uncertainty
surface fitting
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期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
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