Lambert series

In probability theory and statistics, the noncentral F-distribution is a continuous probability distribution that is a generalization of the (ordinary) F-distribution. It describes the distribution of the quotient (X/n₁)/(Y/n₂), where the numerator X has a noncentral chi-squared distribution with n₁ degrees of freedom and the denominator Y has a central chi-squared distribution n₂ degrees of freedom. It is also required that X and Y are statistically independent of each other.

It is the distribution of the test statistic in analysis of variance problems when the null hypothesis is false. The noncentral F-distribution is used to find the power function of such a test.

Occurrence and specification

If $X$ is a noncentral chi-squared random variable with noncentrality parameter $\lambda$ and $\nu _{1}$ degrees of freedom, and $Y$ is a chi-squared random variable with $\nu _{2}$ degrees of freedom that is statistically independent of $X$, then

$F={\frac {X/\nu _{1}}{Y/\nu _{2}}}$

is a noncentral F-distributed random variable. The probability density function for the noncentral F-distribution is

$p(f)=\sum \limits _{k=0}^{\infty }{\frac {e^{-\lambda /2}(\lambda /2)^{k}}{B\left({\frac {\nu _{2}}{2}},{\frac {\nu _{1}}{2}}+k\right)k!}}\left({\frac {\nu _{1}}{\nu _{2}}}\right)^{{\frac {\nu _{1}}{2}}+k}\left({\frac {\nu _{2}}{\nu _{2}+\nu _{1}f}}\right)^{{\frac {\nu _{1}+\nu _{2}}{2}}+k}f^{\nu _{1}/2-1+k}$

when $f\geq 0$ and zero otherwise. The degrees of freedom $\nu _{1}$ and $\nu _{2}$ are positive. The noncentrality parameter $\lambda$ is nonnegative. The term $B(x,y)$ is the beta function, where

$B(x,y)={\frac {\Gamma (x)\Gamma (y)}{\Gamma (x+y)}}.$

The cumulative distribution function for the noncentral F-distribution is

$F(x|d_{1},d_{2},\lambda )=\sum \limits _{j=0}^{\infty }\left({\frac {\left({\frac {1}{2}}\lambda \right)^{j}}{j!}}e^{-{\frac {\lambda }{2}}}\right)I\left({\frac {d_{1}x}{d_{2}+d_{1}x}}{\bigg |}{\frac {d_{1}}{2}}+j,{\frac {d_{2}}{2}}\right)$

where $I$ is the regularized incomplete beta function.

The mean and variance of the noncentral F-distribution are

${\mbox{E}}\left[F\right]={\begin{cases}{\frac {\nu _{2}(\nu _{1}+\lambda )}{\nu _{1}(\nu _{2}-2)}}&\nu _{2}>2\\{\mbox{Does not exist}}&\nu _{2}\leq 2\\\end{cases}}$

and

${\mbox{Var}}\left[F\right]={\begin{cases}2{\frac {(\nu _{1}+\lambda )^{2}+(\nu _{1}+2\lambda )(\nu _{2}-2)}{(\nu _{2}-2)^{2}(\nu _{2}-4)}}\left({\frac {\nu _{2}}{\nu _{1}}}\right)^{2}&\nu _{2}>4\\{\mbox{Does not exist}}&\nu _{2}\leq 4.\\\end{cases}}$

Special cases

When λ = 0, the noncentral F-distribution becomes the F-distribution.

Related distributions

Z has a noncentral chi-squared distribution if

$Z=\lim _{\nu _{2}\to \infty }\nu _{1}F$

where F has a noncentral F-distribution.

Implementations

The noncentral F-distribution is implemented in the R language (e.g., pf function), in MATLAB (ncfcdf, ncfinv, ncfpdf, ncfrnd and ncfstat functions in the statistics toolbox) in Mathematica (NoncentralFRatioDistribution function), in NumPy (random.noncentral_f), and in Boost C++ Libraries.

A collaborative wiki page implements an interactive online calculator, programmed in R language, for noncentral t, chisquare, and F, at the Institute of Statistics and Econometrics, School of Business and Economics, Humboldt-Universität zu Berlin.

References

• Template:Cite web

External links

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