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Depends. Throwing outliers out without thinking is obviously wrong. In many instances outliers can be just invalid measurements and you should ignore them.
by Scea91 11y ago
Depends. Throwing outliers out without thinking is obviously wrong. In many instances outliers can be just invalid measurements and you should ignore them.
- yummyfajitas 11y agoA much better approach is to incorporate measurement error into your statistical procedure. Of course that's usually a lot easier to do with Bayesian techniques...
- dlss 11y ago> In many instances outliers can be just invalid measurements and you should ignore them. signal[i] = value[i] + noise[i]. If you know that value[i] == NaN, then by all means throw out signal[i]. If value[i] != NaN, then you're better off modeling error[i], and using that model to give you information about value[i] as yummyfajitas suggests. This is trivial to see if noise[i] == 0, but for some reason becomes progressively harder for people as noise[i] increases.