Wishart distribution

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Wishart
Probability density function
Cumulative distribution function
Parameters deg. of freedom (real)
scale matrix ( pos. def)
Support is positive definite
Probability density function (pdf)
Cumulative distribution function (cdf)
Mean
Median
Mode
Variance
Skewness
Excess kurtosis
Entropy
Moment-generating function (mgf)
Characteristic function

In statistics, the Wishart distribution, named in honor of John Wishart, is any of a family of probability distributions for nonnegative-definite matrix-valued random variables ("random matrices"). These distributions are of great importance in the estimation of covariance matrices in multivariate statistics.

Definition

Suppose X is an n × p matrix, each row of which is independently drawn from p-variate normal distribution with zero mean:

Then the Wishart distribution is the probability distribution of the p×p random matrix

where S is known as the scatter matrix. One indicates that S has that probability distribution by writing

The positive integer n is the number of degrees of freedom. Sometimes this is written W(Vpn).

If p = 1 and V = 1 then this distribution is a chi-square distribution with n degrees of freedom.

Occurrence

The Wishart distribution arises frequently in likelihood-ratio tests in multivariate statistical analysis. It also arises in the spectral theory of random matrices.

Probability density function

The Wishart distribution can be characterized by its probability density function, as follows.

Let W be a p × p symmetric matrix of random variables that is positive definite. Let V be a (fixed) positive definite matrix of size p × p.

Then, if np, then W has a Wishart distribution with n degrees of freedom if it has a probability density function fW given by

where Γp(·) is the multivariate gamma function defined as

In fact the above definition can be extended to any real n > p − 1.

Characteristic function

The characteristic function of the Wishart distribution is

In other words,

where denotes expectation.

Theorem

If has a Wishart distribution with m degrees of freedom and variance matrix —write —and is a q × p matrix of rank q, then

Corollary 1

If is a nonzero constant vector, then .

In this case, is the chi-square distribution and (note that is a constant; it is positive because is positive definite).

Corollary 2

Consider the case where (that is, the j-th element is one and all others zero). Then corollary 1 above shows that

gives the marginal distribution of each of the elements on the matrix's diagonal.

Noted statistician George Seber points out that the Wishart distribution is not called the "multivariate chi-square distribution" because the marginal distribution of the off-diagonal elements is not chi-square. Seber prefers to reserve the term multivariate for the case when all univariate marginals belong to the same family.

Estimator of the multivariate normal distribution

The Wishart distribution is the probability distribution of the maximum-likelihood estimator (MLE) of the covariance matrix of a multivariate normal distribution. The derivation of the MLE is perhaps surprisingly subtle and elegant. It involves the spectral theorem and the reason why it can be better to view a scalar as the trace of a 1×1 matrix than as a mere scalar. See estimation of covariance matrices.

Drawing values from the distribution

The following procedure is due to Smith & Hocking [1]. One can sample random p × p matrices from a p-variate Wishart distribution with scale matrix and n degrees of freedom (for ) as follows:

  1. Generate a random p × p lower triangular matrix such that:
    • , i.e. is the square root of a sample taken from a chi-square distribution
    • , for , is sampled from a standard normal distribution
  2. Compute the Cholesky decomposition of .
  3. Compute the matrix . At this point, is a sample from the Wishart distribution .

Note that if , the identity matrix, then the sample can be directly obtained from since the Cholesky decomposition of .

See also

de:Wishart-Verteilung it:Variabile casuale di Wishart


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