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Generalized Error Distribution / Generalized Normal

Probability Distributions > Generalized Error Distribution / Generalized Normal

What is a Generalized Error Distribution?

Generalized error distributions (sometimes called generalized normal distributions) are a symmetric family of distributions used in mathematical modeling, usually when errors (the difference between the expected value and the observed values) aren’t normally distributed. Special cases of this distribution are identical to the normal distribution and the Laplace distribution.

The Generalized error distribution is useful when the errors around the mean or in the tails are of special interest. If other deviations from the normal distribution are being studied, other families of distributions can be used. For example, the t-distribution is used if the tails are of interest; the t-distribution approximates the normal distribution as degrees of freedom in the distribution approach infinity.

Three parameters define the distribution:

A generalized normal distribution with Β = 1/2 is equal to the normal distribution; if Β = 1 it is equal to the Double Exponential or Laplace distribution. For values of Β that tend toward zero, the distribution starts to look like a uniform distribution.

generalized error distribution

Several different effects of the shape paramter, Β on the generalized normal distribution. Image: Skbkekas|Wikimedia Commons.

Classes of the Generalized Error Distribution

The two classes of the Generalized Error Distribution have heavy tails or highly skewed tails. Statisticians R. Vasudeva and J. Vasanthat Kumari describe these classes as:

  1. Generalized Error Distributions of the first kind (GED-1). This class has heavy tails.
  2. Generalized Error Distributions of the second kind (GED-2). This class has highly skewed tails.
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      Generalized Error Distribution / Generalized Normal was last modified: December 9th, 2017 by Stephanie Glen