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Simple assumptions can be made about several error components.
In eq. (1) to (4) no error components are included.
Here, this study does so using the allocation parameter of the cross-nested logit model and the overlapping error components of the error components logit model.
Combining the error sensitive coefficient and deformation, the sensitive error components of machine tools are identified.
The Poisson coefficient matrices of each error components are fitted using least squares method.
In this way, different error components can be successfully separated from the sensor outputs.
This is because of the decreased levels of the quantization error components.
In this model an error components specification with spatial error autocorrelation was introduced.
Observations and models may have bias and noise-like error components arising from various error sources.
We then decompose the estimators into the space, time, linearization, and algebraic error components.
Moreover, integrated error components are transformed to coordinate frame of working table for integrated error transformation matrix of machine tools.
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