Regression model performance with noisy dependent variable

Question

I'm doing a support vector regression with the dependent variable representing measurements from an uncalibrated sensor (measurement error between 2% and 20%) and I want to study the effect of this error on the model performance. What's the best method to inject error on the dependent variable samples ? is it sufficient to do this by adding white noise? Any help or suggestion is appreciated

Answer 1

Adding white noise is simple enough and should work, alternatively you could permute the values of your variable, which is another commonly used method.