# Noncentral chi distribution

In probability theory and statistics, the noncentral chi distribution is a noncentral generalization of the chi distribution. It is also known as the generalized Rayleigh distribution.

Parameters $k>0\,$ degrees of freedom $\lambda >0\,$ $x\in [0;+\infty )\,$ ${\frac {e^{-(x^{2}+\lambda ^{2})/2}x^{k}\lambda }{(\lambda x)^{k/2}}}I_{k/2-1}(\lambda x)$ $1-Q_{\frac {k}{2}}\left(\lambda ,x\right)$ with Marcum Q-function $Q_{M}(a,b)$ ${\sqrt {\frac {\pi }{2}}}L_{1/2}^{(k/2-1)}\left({\frac {-\lambda ^{2}}{2}}\right)\,$ $k+\lambda ^{2}-\mu ^{2}$ , where $\mu$ is the mean

## Definition

If $X_{i}$  are k independent, normally distributed random variables with means $\mu _{i}$  and variances $\sigma _{i}^{2}$ , then the statistic

$Z={\sqrt {\sum _{i=1}^{k}\left({\frac {X_{i}}{\sigma _{i}}}\right)^{2}}}$

is distributed according to the noncentral chi distribution. The noncentral chi distribution has two parameters: $k$  which specifies the number of degrees of freedom (i.e. the number of $X_{i}$ ), and $\lambda$  which is related to the mean of the random variables $X_{i}$  by:

$\lambda ={\sqrt {\sum _{i=1}^{k}\left({\frac {\mu _{i}}{\sigma _{i}}}\right)^{2}}}$

## Properties

### Probability density function

The probability density function (pdf) is

$f(x;k,\lambda )={\frac {e^{-(x^{2}+\lambda ^{2})/2}x^{k}\lambda }{(\lambda x)^{k/2}}}I_{k/2-1}(\lambda x)$

where $I_{\nu }(z)$  is a modified Bessel function of the first kind.

### Raw moments

The first few raw moments are:

$\mu _{1}^{'}={\sqrt {\frac {\pi }{2}}}L_{1/2}^{(k/2-1)}\left({\frac {-\lambda ^{2}}{2}}\right)$
$\mu _{2}^{'}=k+\lambda ^{2}$
$\mu _{3}^{'}=3{\sqrt {\frac {\pi }{2}}}L_{3/2}^{(k/2-1)}\left({\frac {-\lambda ^{2}}{2}}\right)$
$\mu _{4}^{'}=(k+\lambda ^{2})^{2}+2(k+2\lambda ^{2})$

where $L_{n}^{(a)}(z)$  is a Laguerre function. Note that the 2$n$ th moment is the same as the $n$ th moment of the noncentral chi-squared distribution with $\lambda$  being replaced by $\lambda ^{2}$ .

## Bivariate non-central chi distribution

Let $X_{j}=(X_{1j},X_{2j}),j=1,2,\dots n$ , be a set of n independent and identically distributed bivariate normal random vectors with marginal distributions $N(\mu _{i},\sigma _{i}^{2}),i=1,2$ , correlation $\rho$ , and mean vector and covariance matrix

$E(X_{j})=\mu =(\mu _{1},\mu _{2})^{T},\qquad \Sigma ={\begin{bmatrix}\sigma _{11}&\sigma _{12}\\\sigma _{21}&\sigma _{22}\end{bmatrix}}={\begin{bmatrix}\sigma _{1}^{2}&\rho \sigma _{1}\sigma _{2}\\\rho \sigma _{1}\sigma _{2}&\sigma _{2}^{2}\end{bmatrix}},$

with $\Sigma$  positive definite. Define

$U=\left[\sum _{j=1}^{n}{\frac {X_{1j}^{2}}{\sigma _{1}^{2}}}\right]^{1/2},\qquad V=\left[\sum _{j=1}^{n}{\frac {X_{2j}^{2}}{\sigma _{2}^{2}}}\right]^{1/2}.$

Then the joint distribution of U, V is central or noncentral bivariate chi distribution with n degrees of freedom. If either or both $\mu _{1}\neq 0$  or $\mu _{2}\neq 0$  the distribution is a noncentral bivariate chi distribution.

## Related distributions

• If $X$  is a random variable with the non-central chi distribution, the random variable $X^{2}$  will have the noncentral chi-squared distribution. Other related distributions may be seen there.
• If $X$  is chi distributed: $X\sim \chi _{k}$  then $X$  is also non-central chi distributed: $X\sim NC\chi _{k}(0)$ . In other words, the chi distribution is a special case of the non-central chi distribution (i.e., with a non-centrality parameter of zero).
• A noncentral chi distribution with 2 degrees of freedom is equivalent to a Rice distribution with $\sigma =1$ .
• If X follows a noncentral chi distribution with 1 degree of freedom and noncentrality parameter λ, then σX follows a folded normal distribution whose parameters are equal to σλ and σ2 for any value of σ.