How do you Winsorize an outlier?

How do you Winsorize an outlier?

A Basic Method to Winsorize by Hand

1. Analyze your data to make sure the outlier isn’t a result of measurement error or some other fixable error.
2. Decide how much Winsorization you want.
3. Replace the extreme values by the maximum and/or minimum values at the threshold.

How do you trim outliers?

Outlier Removal

1. Do nothing and leave the data unadjusted.
2. Discard all of the outliers. The removal of extreme values is usually called trimming or truncation.
3. Replace all of the outliers with the largest value that is not considered an outlier. The replacement of extreme values is usually called winsorization.

How are the mean and standard deviations used in Winsorizing?

The mean and the standard deviation are two common ways to measure the location of the center of a dataset and the spread of observations in a dataset, respectively. However, these two metrics can both be influenced by extreme outliers. Thus, winsorizing data allows us to set extreme outliers equal to less extreme values.

Which is an example of a 90% winsorization?

For example, a 90% winsorization sets all observations greater than the 95th percentile equal to the value at the 95th percentile and all observations less than the 5th percentile equal to the value at the 5th percentile. In effect, to winsorize data means to change extreme values in a dataset to less extreme values.

Which is the best article on winsorization and trimming?

I consulted the Encyclopedia of Statistical Sciences (Kotz et al. (Eds), 2nd Ed, 2006) which has an article “Trimming and Winsorization ” by David Ruppert (Vol 14, p. 8765). According to the article: Winsorizaion is symmetric: Some people want to modify only the large data values.

Which is the best way to define an outlier?

Outliers can be defined in several ways, the classical one being something that is generated by some other process than that which you interested in. Another definition is in terms of absolute extremeness – which may be problematic because in some situations the extreme values carry the information that most matters to a particular problem.