- Wallenius' noncentral hypergeometric distribution
**Introduction**

right|thumb|300px">

Probability mass function for Wallenius' Noncentral Hypergeometric Distribution for different values of the odds ratio ω.

m_{1}= 80, m_{2}= 60, n = 100, ω = 0.1 ... 20In

probability theory andstatistics ,**Wallenius' noncentral hypergeometric distribution**is a generalization of thehypergeometric distribution where items are sampled with bias.This distribution can be illustrated as an urn model with bias. 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 ω_{1}and each white ball has the weight ω_{2}. We will say that the odds ratio is ω = ω_{1}/ ω_{2}. Now we are taking "n" balls, one by one, in such a way that the probability of taking a particular ball at a particular draw is equal to its proportion of the total weight of all balls that lie in the urn at that moment. The number of red balls "x"_{1}that we get in this experiment is a random variable with Wallenius' noncentral hypergeometric distribution.The matter is complicated by the fact that there is more than one noncentral hypergeometric distribution. Wallenius' noncentral hypergeometric distribution is obtained if balls are sampled one by one in such a way that there is

competition between the balls.Fisher's noncentral hypergeometric distribution is obtained if the balls are sampled simultaneously or independently of each other. 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.The two distributions are both equal to the (central)

hypergeometric distribution when the odds ratio is 1.It is far from obvious why these two distributions are different. See the Wikipedia entry on

noncentral hypergeometric distributions for a more detailed explanation of the difference between these two probability distributions.

**Univariate distribution**Probability distribution

name =Univariate Wallenius' Noncentral Hypergeometric Distribution

type =mass

pdf_

cdf_

parameters =$m\_1,\; m\_2\; in\; mathbb\{N\}$

$N\; =\; m\_1\; +\; m\_2$

$n\; in\; [0,N)$

$omega\; in\; mathbb\{R\}\_+$

support =$x\; in\; [x\_\{min\},x\_\{max\}]$

$x\_\{min\}=max(0,n-m\_2)$

$x\_\{max\}=min(n,m\_1)$

pdf =$inom\{m\_1\}\{x\_1\}\; inom\{m\_2\}\{x\_2\}\; int\_0^1\; (1-t^\{omega/D\})^\{x\_1\}\; (1-t^\{1/D\})^\{x\_2\}\; operatorname\{d\}t$

where $D=omega(m\_1-x\_1)+(m\_2-x\_2)$

cdf =

mean =Approximated by solution $mu$ to

$frac\{mu\}\{m\_1\}\; +\; left(1-frac\{n-mu\}\{m\_2\}\; ight)^\{omega\}\; =\; 1$

median =

mode =

variance =$approx\; frac\{Nab\}\{(N-1)(m\_1\; b\; +\; m\_2\; a)\},$, where

$a=mu(m\_1-mu),;\; b=(n-mu)(mu+m\_2-n)$

skewness =

kurtosis =

entropy =

mgf =

char =Wallenius' distribution is particularly complicated because each ball has a probability of being taken that depends not only on its weight, but also on the total weight of its competitors. And the weight of the competing balls depends on the outcomes of all preceding draws.

This recursive dependency gives rise to a

difference equation with a solution that is given in open form by the integral in the expression of the probability mass function in the table above.Closed form expressions for the probability mass function exist (Lyons, 1980), but they are not very useful for practical calculations because of extreme numerical instability, except in degenerate cases.

Several other calculation methods are used, including

recursion , Taylor expansion andnumerical integration (Fog, 2007, 2008).The most reliable calculation method is recursive calculation of f("x","n") from f("x","n"-1) and f("x"-1,"n"-1) using the recursion formula given below under properties. The probabilities of all ("x","n") combinations on all possible trajectories leading to the desired point are calculated, starting with f(0,0) = 1 as shown on the figure to the right. The total number of probabilities to calculate is "n"("x"+1)-"x"

^{2}. Other calculation methods must be used when "n" and "x" are so big that this method is too inefficient.The probability that all balls have the same color is easier to calculate. See the formula below under multivariate distribution.

No exact formula for the mean is known (short of complete enumeration of all probabilities). The equation given above is reasonably accurate. This equation can be solved for μ by Newton-Raphson iteration. The same equation can be used for estimating the odds from an experimentally obtained value of the mean.

**Multivariate distribution**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.

Probability distribution

name =Multivariate Wallenius' Noncentral Hypergeometric Distribution

type =mass

pdf_

cdf_

parameters =$c\; in\; mathbb\{N\}$

$mathbf\{m\}=(m\_1,ldots,m\_c)\; in\; mathbb\{N\}^c$

$N\; =\; sum\_\{i=1\}^c\; m\_i$

$n\; in\; [0,N)$

$\backslash boldsymbol\{omega\}\; =\; (omega\_1,ldots,omega\_c)\; in\; mathbb\{R\}\_+^c$

support =$mathrm\{S\}\; =\; left\{\; mathbf\{x\}\; in\; mathbb\{Z\}\_\{0+\}^c\; ,\; :\; ,\; sum\_\{i=1\}^\{c\}\; x\_i\; =\; n\; ight\}$

pdf =$left(prod\_\{i=1\}^c\; inom\{m\_i\}\{x\_i\}\; ight)\; int\_0^1\; prod\_\{i=1\}^c\; (1-t^\{omega\_i/D\})^\{x\_i\}\; operatorname\{d\}t,,$

where $D=\backslash boldsymbol\{omega\}cdot\; (mathbf\{m\}-mathbf\{x\})\; =\; sum\_\{i=1\}^c\; omega\_i(m\_i-x\_i)$

cdf =

mean =Approximated by solution $mu\_1,ldots,mu\_c$ to

$left(1-frac\{mu\_1\}\{m\_1\}\; ight)^\{1/omega\_1\}\; =\; left(1-frac\{mu\_2\}\{m\_2\}\; ight)^\{1/omega\_2\}\; =\; ldots\; =\; left(1-frac\{mu\_c\}\{m\_c\}\; ight)^\{1/omega\_c\}$

$wedge\; ,\; sum\_\{i=1\}^c\; mu\_i\; =\; n\; ,\; wedge\; ,\; forall,\; i\; in\; [0,c]\; ,\; :,\; 0\; le\; mu\_i\; le\; m\_i,.$

median =

mode =

variance =Approximated by variance ofFisher's noncentral hypergeometric distribution with same mean.

skewness =

kurtosis =

entropy =

mgf =

char =The probability mass function can be calculated by various Taylor expansion methods or by

numerical integration (Fog, 2008).The probability that all balls have the same color, "j", can be calculated as::$operatorname\{mwnchypg\}((0,ldots,0,x\_j,0,ldots);n,mathbf\{m\},\; \backslash boldsymbol\{omega\})\; =\; frac\{m\_j^\{,,underline\{n\}\; \{left(\; frac\{1\}\{omega\_j\}sum\_\{i=1\}^\{c\}m\_iomega\_i\; ight)\; ^\{underline\{n\}$for "x"

_{j}= "n" ≤ "m"_{j}, where the underlined superscript denotes the falling factorial as defined above.A reasonably good approximation to the mean can be calculated using the equation given above. The equation can be solved by defining θ so that:$mu\_i\; =\; m\_i(1-e^\{omega\_i\; heta\})$and solving:$sum\_\{i=1\}^c\; mu\_i\; =\; n$for θ by Newton-Raphson iteration.

The equation for the mean is also useful for estimating the odds from experimentally obtained values for the mean.

No good way of calculating the variance is known. The best known method is to approximate the multivariate Wallenius distribution by a multivariate

Fisher's noncentral hypergeometric distribution with the same mean, and insert the mean as calculated above in the approximate formula for the variance of the latter distribution.**Properties**The order of the colors is arbitrary so that any colors can be swapped.

The weights can be arbitrarily scaled::$operatorname\{mwnchypg\}(mathbf\{x\};n,mathbf\{m\},\; \backslash boldsymbol\{omega\})\; =\; operatorname\{mwnchypg\}(mathbf\{x\};n,mathbf\{m\},\; r\backslash boldsymbol\{omega\}),,$ 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::$operatorname\{mwnchypg\}left(mathbf\{x\};n,mathbf\{m\},\; (omega\_1,ldots,omega\_\{c-1\},omega\_\{c-1\})\; ight),\; =$::$operatorname\{mwnchypg\}left((x\_1,ldots,x\_\{c-1\}+x\_c);\; n,(m\_1,ldots,m\_\{c-1\}+m\_c),\; (omega\_1,ldots,omega\_\{c-1\})\; ight),\; cdot$::$operatorname\{hypg\}(x\_c;\; x\_\{c-1\}+x\_c,\; m\_c,\; m\_\{c-1\}+m\_c),,$where $operatorname\{hypg\}(x;n,m,N)$ is the (univariate, central) hypergeometric distribution probability.

**Complementary Wallenius' noncentral hypergeometric distribution**

right|thumb|300px">

Probability mass function for the Complementary Wallenius' Noncentral Hypergeometric Distribution for different values of the odds ratio ω.

m_{1}= 80, m_{2}= 60, n = 40, ω = 0.05 ... 10The balls that are "not" taken in the urn experiment have a distribution that is different from Wallenius' noncentral hypergeometric distribution, due to a lack of symmetry. The distribution of the balls not taken can be called the

**complementary Wallenius' noncentral hypergeometric distribution**.Probabilities in the complementary distribution are calculated from Wallenius' distribution by replacing "n" with "N"-"n", "x"

_{i}with "m"_{i}- "x"_{i}, and ω_{i}with 1/ω_{i}.**oftware available*** An implementation for the R programming language is available as the package named [

*http://cran.stat.ucla.edu/src/contrib/Descriptions/BiasedUrn.html BiasedUrn*] . Includes univariate and multivariate probability mass functions, distribution functions, quantiles,random variable generating functions, mean and variance.

* Implementation inC++ is available from [*http://www.agner.org/random/ www.agner.org*] .**ee also***

Noncentral hypergeometric distributions

*Fisher's noncentral hypergeometric distribution

*Hypergeometric distribution

* Urn models

*Biased sample

* Bias

*Population genetics

*Fisher's exact test **References**Citation

last=Chesson

first=J.

year=1976

title=A non-central multivariate hypergeometric distribution arising from biased sampling with application to selective predation

periodical=Journal of Applied Probability

volume=13

pages=795-797.Citation

last=Fog

first=A.

year=2007

title=Random number theory

url=http://www.agner.org/random/theory/.Citation

last=Fog

first=A.

year=2008

title=Calculation Methods for Wallenius' Noncentral Hypergeometric Distribution

periodical=Communications In statictics, Simulation and Computation

volume=37

issue=2

pages=258-273.Citation

last=Johnson

first=N. L.

last2=Kemp

first2=A. W.

last3=Kotz

first3=S.

author-link=

year=2005

title=Univariate Discrete Distributions

publisher=Wiley and Sons

place=Hoboken, New Jersey.Citation

last=Lyons

first=N. I.

year=1980

title=Closed Expressions for Noncentral Hypergeometric Probabilities

periodical=Communications In statictics, B

volume=9

pages=313-314.Citation

last=Manly

first=B. F. J.

year=1974

title=A Model for Certain Types of Selection Experiments

periodical=Biometrics

volume=30

pages=281-294.Citation

last=Wallenius

first=K. T.

year=1963

title=Biased Sampling: The Non-central Hypergeometric Probability Distribution. Ph.D. Thesis

publisher=Stanford University, Department of Statistics.

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