In probability theory and statistics, a shape parameter is a kind of numerical parameter of a parametric family of probability distributions.[1]
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Definition
A shape parameter is any parameter of a probability distribution that is neither a location parameter nor a scale parameter (nor a function of either of both or these only, such as a rate parameter). Such a parameter must affect the shape of a distribution rather than simply shifting it (as a location parameter does) or stretching/shrinking it (as a scale parameter does).
Examples
The following continuous probability distributions have a shape parameter:
- Beta distribution
- Burr distribution
- Erlang distribution
Exponential power distribution - Gamma distribution
- Generalized extreme value distribution
- Log-logistic distribution
- Inverse-gamma distribution
- Pareto distribution
- Pearson distribution
- Weibull distribution
By contrast, the following continuous distributions do not have a shape parameter, so their shape is fixed and only their location or their scale or both can change. It follows that (where they exist) the skewness and kurtosis of these distribution are constants, as skewness and kurtosis are independent of location and scale parameters.
- Exponential distribution
- Cauchy distribution
- Logistic distribution
- Normal distribution
- Raised cosine distribution
- Uniform distribution
- Wigner semicircle distribution
See also
References
- ^ Everitt B.S. (2002) Cambridge Dictionary of Statistics. 2nd Edition. CUP. ISBN 0-521-81099-x
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