One common method of construction of a multivariate t-distribution, for the case of dimensions, is based on the observation that if and are independent and distributed as and respectively, the matrix is a p × p matrix, and, then has the density and is said to be distributed as a multivariate t-distribution with parameters. Note that is not the covariance matrix since the covariance is given by . In the special case, the distribution is a multivariate Cauchy distribution.
Derivation
There are in fact many candidates for the multivariate generalization of Student's t-distribution. An extensive survey of the field has been given by Kotz and Nadarajah. The essential issue is to define a probability densityfunction of several variables that is the appropriate generalization of the formula for the univariate case. In one dimension, with and, we have the probability density function and one approach is to write down a corresponding function of several variables. This is the basic idea of elliptical distribution theory, where one writes down a corresponding function of variables that replaces by a quadratic function of all the. It is clear that this only makes sense when all the marginal distributions have the same degrees of freedom. With, one has a simple choice of multivariate density function which is the standard but not the only choice. An important special case is the standard bivariate t-distribution, p = 2: Note that. Now, if is the identity matrix, the density is The difficulty with the standard representation is revealed by this formula, which does not factorize into the product of the marginal one-dimensional distributions. When is diagonal the standard representation can be shown to have zero correlation but the marginal distributions do not agree with statistical independence.
The definition of the cumulative distribution function in one dimension can be extended to multiple dimensions by defining the following probability : There is no simple formula for, but it can be via Monte Carlo integration.
Copulas based on the multivariate ''t''
The use of such distributions is enjoying renewed interest due to applications in mathematical finance, especially through the use of the Student's t copula.
Related concepts
In univariate statistics, the Student's t-test makes use of Student's t-distribution. Hotelling's T-squared distribution is a distribution that arises in multivariate statistics. The matrix t-distribution is a distribution for random variables arranged in a matrix structure.
