Outlier Detection

In recent times, outlier detection has become increasingly relevant in various contexts. Outlier Detection on skewed Distributions - Cross Validated. Under a classical definition of an outlier as a data point outide the 1.5* IQR from the upper or lower quartile, there is an assumption of a non-skewed distribution. For skewed distributions (Expon... Another key aspect involves, how to use PCA to detect outliers?

Start asking to get answers Find the answer to your question by asking. Ask question pca outliers dimensionality-reduction anomaly-detection Simple outlier detection for time series - Cross Validated. I wanted to generate a very simple example of anomaly detection for time series.

Similarly, so I created sample data with one very obvious outlier. Here's a picture of the data: The problem is, I didn't get any Finding outliers without assuming normal distribution.

There are statistical tests for outlier's like Grubbs' test and Dixon's ratio test that I have referred to on another post. Equally important, they assume the population distribution is normal although Dixon's test is robust to the normality assumption in small samples. A boxplot is a nice informal way to spot outliers in your data.

Detecting outliers using standard deviations - Cross Validated. Following my question here, I am wondering if there are strong views for or against the use of standard deviation to detect outliers (e.g. any datapoint that is more than 2 standard deviation is an outlier). Outlier detection in very small sets - Cross Validated.

Outlier detection in very small sets Ask Question Asked 11 years, 11 months ago Modified 3 years, 10 months ago MAD formula for outlier detection - Cross Validated. This perspective suggests that, mAD formula for outlier detection Ask Question Asked 11 years ago Modified 8 months ago

In this context, determine outliers using IQR or standard deviation?. After the outlier detection the data will be sent to people who check for outliers which just fell outside the boundaries. When googling for determine outliers it shows how to determine outliers using the Inter Quartile Range (IQR).

Building on this, outlier detection using regression - Cross Validated. Can regression be used for out lier detection. I understand that there are ways to improve a regression model by removing the outliers.

But the primary aim here is not to fit a regression model but... Simple algorithm for online outlier detection of a generic time series. The outlier test is on the residuals, so hopefully the time-dependence is small.