Outlier Detection The z-score is used to identify outliers in a dataset. Any data point with a z-score greater than 3 or less than -3 is considered an outlier.

At least 5% of data even in a reasonably high quality data set will likely contain anomalies—odd as these data might be, it is more peculiar not to find them than to identify them. More formally ...

Explore the importance of robust statistics like median and MAD in data analysis, ensuring accurate insights despite outliers ...

Robust or nonparametric statistical methods are alternative methods for analysis. Robust statistical methods such as weighted least-squares regression minimize the effect of an outlier observation (3) ...

But z-tests usually require large data sets. Conformance runs from early commercial lots usually produce small data sets, and the standard deviation of the population is not usually known. We show, ...

Outlier detection is an integral component of statistical modelling and estimation. For highdimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose ...