Noncentral F-distribution

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.

Contents

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 [1]

p(f) =\sum\limits_{k=0}^\infty\frac{e^{-\lambda/2}(\lambda/2)^k}{ B\left(\frac{\nu_2}{2},\frac{\nu_1}{2}%2Bk\right) k!} \left(\frac{\nu_1}{\nu_2}\right)^{\frac{\nu_1}{2}%2Bk} \left(\frac{\nu_2}{\nu_2%2B\nu_1f}\right)^{\frac{\nu_1%2B\nu_2}{2}%2Bk}f^{\nu_1/2-1%2Bk}

when f\ge0 and zero otherwise. The degrees of freedom \nu_1 and \nu_2 are positive. The noncentrailty parameter \lambda is nonnegative. The term B(x,y) is the beta function, where

B(x,y)=\frac{\Gamma(x)\Gamma(y)}{\Gamma(x%2By)}.

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_1F}{d_2 %2B d_1F}\bigg|\frac{d_1}{2}%2Bj,\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%2B\lambda)}{\nu_1(\nu_2-2)} &\nu_2>2\\ \mbox{Does not exist} &\nu_2\le2\\ \end{cases}

and

\mbox{Var}\left[F\right]= \begin{cases} 2\frac{(\nu_1%2B\lambda)^2%2B(\nu_1%2B2\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\le4.\\ \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.[2]

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.[3]

Notes

  1. ^ S. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, (New Jersey: Prentice Hall, 1998), p. 29.
  2. ^ John Maddock, Paul A. Bristow, Hubert Holin, Xiaogang Zhang, Bruno Lalande, Johan Råde. "Noncentral F Distribution: Boost 1.39.0". Boost.org. http://www.boost.org/doc/libs/1_39_0/libs/math/doc/sf_and_dist/html/math_toolkit/dist/dist_ref/dists/nc_f_dist.html. Retrieved 20 August 2011. 
  3. ^ Sigbert Klinke (10 December 2008). "Comparison of noncentral and central distributions". Humboldt-Universität zu Berlin. http://mars.wiwi.hu-berlin.de/mediawiki/slides/index.php/Comparison_of_noncentral_and_central_distributions. 

References

External links