Fisher's noncentral hypergeometric distribution

Fisher's noncentral hypergeometric distribution

Probability mass function for Fisher's noncentral hypergeometric distribution for different values of the odds ratio ω.
"m"1 = 80, "m"2 = 60, "n" = 100, ω = 0.01, ..., 1000

In probability theory and statistics, Fisher's noncentral hypergeometric distribution is a generalization of the hypergeometric distribution where sampling probabilities are modified by weight factors. Fisher's noncentral hypergeometric distribution can also be defined as the conditional distribution of two or more binomially distributed variables dependent upon their fixed sum.

The distribution may be illustrated by the following urn model. Assume, for example, that an urn contains "m"1 red balls and "m"2 white balls, totalling "N" = "m"1 + "m"2 balls. Each red ball has the weight ω1 and each white ball has the weight ω2. We will say that the odds ratio is ω = ω1 / ω2. Now we are taking balls randomly in such a way that the probability of taking a particular ball is proportional to its weight, but independent of what happens to the other balls. The number of balls taken of a particular color follows the binomial distribution. If the total number "n" of balls taken is known then the conditional distribution of the number of taken red balls for given "n" is Fisher's noncentral hypergeometric distribution. To generate this distribution experimentally, we have to repeat the experiment until it happens to give "n" balls.

If we want to fix the value of "n" prior to the experiment then we have to take the balls one by one until we have "n" balls. The balls are therefore no longer independent. This gives a slightly different distribution known as Wallenius' noncentral hypergeometric distribution. It is far from obvious why these two distributions are different. See the entry for noncentral hypergeometric distributions for an explanation of the difference between these two distributions and a discussion of which distribution to use in various situations.

The two distributions are both equal to the (central) hypergeometric distribution when the odds ratio is 1.

Unfortunately, both distributions are known in the literature as "the" noncentral hypergeometric distribution. It is important to be specific about which distribution is meant when using this name.

Fisher's noncentral hypergeometric distribution was first given the name extended hypergeometric distribution (Harkness, 1965), but this name is rarely used today.

Univariate distribution

Probability distribution
name =Univariate Fisher's noncentral hypergeometric distribution
type =mass


parameters =m_1, m_2 in mathbb{N}
N = m_1 + m_2
n in [0,N)
omega in mathbb{R}_+
support =x in [x_min,x_max]
pdf =frac{inom{m_1}{x} inom{m_2}{n-x} omega^x}{P_0}
where P_0 = sum_{y=x_min}^{x_max} inom{m_1}{y} inom{m_2}{n-y} omega^y
cdf =
mean =frac{P_1}{P_0}, where P_k = sum_{y=x_min}^{x_max} inom{m_1}{y} inom{m_2}{n-y} omega^y, y^k
median =
mode =,, leftlfloor frac{-2C}{B - sqrt{B^2-4AC
ight floor , , where A=omega-1, B = m_1 + n - N -(m_1+n+2)omega, C = (m_1+1)(n+1)omega.
variance =frac{P_2}{P_0} - left( frac{P_1}{P_0} ight)^2, where "P""k" is given above.
skewness =
kurtosis =
entropy =
mgf =
char =

The probability function, mean and variance are given in the table to the right.

An alternative expression of the distribution has both the number of balls taken of each color and the number of balls not taken as random variables, whereby the expression for the probability becomes symmetric.

The calculation time for the probability function can be high when the sum in "P"0 has many terms. The calculation time can be reduced by calculating the terms in the sum recursively relative to the term for "y" = "x" and ignoring negligible terms in the tails (Liao and Rosen, 2001).

The mean can be approximated by::mu approx frac{-2c}{b - sqrt{b^2-4ac , ,where a=omega-1, b=m_1 + n - N -(m_1+n)omega, c=m_1 n omega.

The variance can be approximated by::sigma^2 approx frac{N}{N-1} igg/ left( frac{1}{mu}+ frac{1}{m_1-mu}+ frac{1}{n-mu}+ frac{1}{mu+m_2-n} ight) .

Better approximations to the mean and variance are given by Levin (1984), Liao (1992), McCullagh and Nelder (1989).


The following symmetry relations apply:

:operatorname{fnchypg}(x;n,m_1,N,omega) = operatorname{fnchypg}(n-x;n,m_2,N,1/omega),.

:operatorname{fnchypg}(x;n,m_1,N,omega) = operatorname{fnchypg}(x;m_1,n,N,omega),.

:operatorname{fnchypg}(x;n,m_1,N,omega) = operatorname{fnchypg}(m_1-x;N-n,m_1,N,1/omega),.

Recurrence relation:

:operatorname{fnchypg}(x;n,m_1,N,omega) = operatorname{fnchypg}(x-1;n,m_1,N,omega) frac{(m_1-x+1)(n-x+1)}{x(m_2-n+x)}omega,.

Multivariate distribution

Probability distribution
name =Multivariate Fisher's Noncentral Hypergeometric Distribution
type =mass


parameters =c in mathbb{N}
mathbf{m}=(m_1,ldots,m_c) in mathbb{N}^c
N = sum_{i=1}^c m_i
n in [0,N)
oldsymbol{omega} = (omega_1,ldots,omega_c) in mathbb{R}_+^c
support =mathrm{S} = left{ mathbf{x} in mathbb{Z}_{0+}^c , : , sum_{i=1}^{c} x_i = n ight}
pdf =frac{1}{P_0}prod_{i=1}^{c} inom{m_i}{x_i}omega_i^{x_i}
where P_0 = sum_{(y_0,ldots,y_c)in mathrm{S
prod_{i=1}^{c} inom{m_i}{y_i}omega_i^{y_i}
cdf =
mean =The mean μi of "x"i can be approximated by
mu_i = frac{m_i r omega_i}{r omega_i + 1} where "r" is the unique positive solution to sum_{i=1}^{c}mu_i = n,.
median =
mode =
variance =
skewness =
kurtosis =
entropy =
mgf =
char =

The distribution can be expanded to any number of colors "c" of balls in the urn. The multivariate distribution is used when there are more than two colors.

The probability function and a simple approximation to the mean are given to the right. Better approximations to the mean and variance are given by McCullagh and Nelder (1989).


The order of the colors is arbitrary so that any colors can be swapped.

The weights can be arbitrarily scaled:

:operatorname{mfnchypg}(mathbf{x};n,mathbf{m}, oldsymbol{omega}) = operatorname{mfnchypg}(mathbf{x};n,mathbf{m}, roldsymbol{omega}),, for all r in mathbb{R}_+.

Colors with zero number ("m""i" = 0) or zero weight (ω"i" = 0) can be omitted from the equations.

Colors with the same weight can be joined:

:egin{align}& {} operatorname{mfnchypg}left(mathbf{x};n,mathbf{m}, (omega_1,ldots,omega_{c-1},omega_{c-1}) ight) \\& {} = operatorname{mfnchypg}left((x_1,ldots,x_{c-1}+x_c); n,(m_1,ldots,m_{c-1}+m_c), (omega_1,ldots,omega_{c-1}) ight), cdot \\& qquad operatorname{hypg}(x_c; x_{c-1}+x_c, m_c, m_{c-1}+m_c)end{align}

where operatorname{hypg}(x;n,m,N) is the (univariate, central) hypergeometric distribution probability.


Fisher's noncentral hypergeometric distribution is useful for models of biased sampling or biased selection where the individual items are sampled independently of each other with no competition. The bias or odds can be estimated from an experimental value of the mean. Use Wallenius' noncentral hypergeometric distribution instead if items are sampled one by one with competition.

Fisher's noncentral hypergeometric distribution is used mostly for tests in contingency tables where a conditional distribution for fixed margins is desired. This can be useful e.g. for testing or measuring the effect of a medicine. See McCullagh and Nelder (1989).

oftware available

* An implementation for the R programming language is available as the package named [ BiasedUrn] . Includes univariate and multivariate probability mass functions, distribution functions, quantiles, random variable generating functions, mean and variance.
* The R package [ MCMCpack] includes the univariate probability mass function and random variable generating function.
* SAS System includes univariate probability mass function and distribution function.
* Implementation in C++ is available from [] .
* Calculation methods are described by Liao and Rosen (2001) and Fog (2008).

ee also

* Noncentral hypergeometric distributions
* Wallenius' noncentral hypergeometric distribution
* Hypergeometric distribution
* Urn models
* Biased sample
* Bias
* Contingency table
* Fisher's exact test


first=N. L.
first2=A. W.
title=Univariate Discrete Distributions
publisher=Wiley and Sons
place=Hoboken, New Jersey

first2=J. A.
title=Generalized Linear Models, 2. ed.
publisher=Chapman and Hall

first=N. E.
first2=N. E.
title=Statistical Methods in Cancer Research
publisher=International Agency for Research on Cancer

title=Random number theory

title=Sampling Methods for Wallenius' and Fisher's Noncentral Hypergeometric Distributions
periodical=Communications In statictics, Simulation and Computation

first=J. G.
title=Fast and Stable Algorithms for Computing and Sampling from the Noncentral Hypergeometric Distribution
periodical=The American Statistician

title=An Algorithm for the Mean and Variance of the Noncentral Hypergeometric Distribution

title=Simple Improvements on Cornfield's approximation to the mean of a noncentral Hypergeometric random variable

Wikimedia Foundation. 2010.

Look at other dictionaries:

  • Wallenius' noncentral hypergeometric distribution — Introduction Probability mass function for Wallenius Noncentral Hypergeometric Distribution for different values of the odds ratio ω. m1 = 80, m2 = 60, n = 100, ω = 0.1 ... 20In probability theory and statistics, Wallenius noncentral… …   Wikipedia

  • Noncentral hypergeometric distributions — In statistics, the hypergeometric distribution is the discrete probability distribution generated by picking colored balls at random from an urn without replacement. Various generalizations to this distribution exist for cases where the picking… …   Wikipedia

  • Hypergeometric distribution — Hypergeometric parameters: support: pmf …   Wikipedia

  • Noncentral t-distribution — Noncentral Student s t Probability density function parameters: degrees of freedom noncentrality parameter support …   Wikipedia

  • 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… …   Wikipedia

  • Noncentral chi-squared distribution — Noncentral chi squared Probability density function Cumulative distribution function parameters …   Wikipedia

  • Chi-squared distribution — This article is about the mathematics of the chi squared distribution. For its uses in statistics, see chi squared test. For the music group, see Chi2 (band). Probability density function Cumulative distribution function …   Wikipedia

  • Probability distribution — This article is about probability distribution. For generalized functions in mathematical analysis, see Distribution (mathematics). For other uses, see Distribution (disambiguation). In probability theory, a probability mass, probability density …   Wikipedia

  • Multinomial distribution — Multinomial parameters: n > 0 number of trials (integer) event probabilities (Σpi = 1) support: pmf …   Wikipedia

  • Discrete phase-type distribution — The discrete phase type distribution is a probability distribution that results from a system of one or more inter related geometric distributions occurring in sequence, or phases. The sequence in which each of the phases occur may itself be a… …   Wikipedia