Tool wear, catalyst aging and harmonic cycling are type of industrial problems that can be handled by monitoring regression coefficients. We derive Fisher´s score function assuming Normal and Extreme-Value errors and propose this as the optimal monitoring function to detect small changes in the regression parameters. This is illustrated in a simulation study.
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