# Normal-exponential-gamma distribution

In probability theory and statistics, the normal-exponential-gamma distribution (sometimes called the NEG distribution) is a three-parameter family of continuous probability distributions. It has a location parameter $\mu$ , scale parameter $\theta$ and a shape parameter $k$ .

Parameters μ ∈ R — mean (location)$k>0$ shape $\theta >0$ scale $x\in (-\infty ,\infty )$ $\propto \exp {\left({\frac {(x-\mu )^{2}}{4\theta ^{2}}}\right)}D_{-2k-1}\left({\frac {|x-\mu |}{\theta }}\right)$ $\mu$ $\mu$ $\mu$ ${\frac {\theta ^{2}}{k-1}}$ for $k>1$ 0

## Probability density function

The probability density function (pdf) of the normal-exponential-gamma distribution is proportional to

$f(x;\mu ,k,\theta )\propto \exp {\left({\frac {(x-\mu )^{2}}{4\theta ^{2}}}\right)}D_{-2k-1}\left({\frac {|x-\mu |}{\theta }}\right)$ ,

where D is a parabolic cylinder function.

As for the Laplace distribution, the pdf of the NEG distribution can be expressed as a mixture of normal distributions,

$f(x;\mu ,k,\theta )=\int _{0}^{\infty }\int _{0}^{\infty }\ \mathrm {N} (x|\mu ,\sigma ^{2})\mathrm {Exp} (\sigma ^{2}|\psi )\mathrm {Gamma} (\psi |k,1/\theta ^{2})\,d\sigma ^{2}\,d\psi ,$ where, in this notation, the distribution-names should be interpreted as meaning the density functions of those distributions.

### Applications

The distribution has heavy tails and a sharp peak at $\mu$ and, because of this, it has applications in variable selection.

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