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In cases where the reason for the outliers is known, it may be doable to incorporate this effect into the model construction, for example through the use of a hierarchical Bayes model, or a mixture mannequin. A frequent trigger of outliers is a mixture of two distributions, which could also be two distinct sub-populations, or could indicate 'right trial' versus 'measurement error'; that is modeled by a mixture mannequin. Outliers can occur by probability in any distribution, however they can point out novel behaviour or buildings in the information-set, measurement error, or that the population has a heavy-tailed distribution. In the case of measurement error, one wishes to discard them or use statistics which are sturdy to outliers, while within the case of heavy-tailed distributions, they point out that the distribution has excessive skewness and that one needs to be very cautious in utilizing tools or intuitions that assume a traditional distribution. Even when a standard distribution model is acceptable to the information being analyzed, outliers are expected for giant sample sizes and mustn't mechanically be discarded if that's the case. Some are graphical reminiscent of regular likelihood plots

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