To eliminate values that are not representative of the process that we are attempting to measure or describe.
Parameter estimation hinges on finding the solution that minimizes the error sum of squares.
Incorrect parameter estimation can be avoided by robust procedures. However, Robust Regression only deals with one-time values, i.e. pulses. In the absence of robust procedures, early efforts in applied statistics tried to perform preliminary screening, but often reported failure. General and largely unsatisfactory remarks on unusual values tended to diminish the magnitude of the problem through errors of omission . Time series data offers opportunities, and pitfalls not found in cross-sectional data.