Backward elimination is used to determine which of the model components are necessary to retain in order to best account for the data.
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Efficient algorithms such as forward selection and backward elimination have been produced for FFDs, but due to the complexity of aliasing introduced by distinguishing control and noise variables, these methods have not yet been proven effective for RPDs.
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