Introduction to outliers

An outlier can be defined as any observation that does not fit a pattern . In a typical PAT calibration problem, one can encounter three different types of outliers  

It is very important to note that the term outlier does not imply incorrect . An outlier could be caused by an error or an incorrect action, but it could just as easily be caused by a real phenomenon that is relevant to the problem. 

Outliers demand special attention in chemometrics for several different reasons. During model development, their extremeness often gives them an unduly high influence in the calculation of the calibration model. Therefore, if they represent erroneous readings, then they will add disproportionately more error to the calibration model. Furthermore, even if they represent informative information, it might be determined that this specific information is irrelevant to the problem. Outliers are also very important during model deployment, because they can be informative indicators of specific failures or abnormalities in the process being sampled, or in the measurement system itself. This use of outlier detection is discussed in the Model Deployment section (12.10), later in this chapter. 

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