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Multi-dimensional distribution

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multivariate distribution

A probability distribution on the - algebra of Borel sets of an s - dimensional Euclidean space \mathbf R ^ {s} . One usually speaks of a multivariate distribution as the distribution of a multi-dimensional random variable, or random vector, X = ( X _ {1} \dots X _ {s} ) , meaning by this the joint distribution of the real random variables X _ {1} ( \omega ) \dots X _ {s} ( \omega ) given on the same space of elementary events \Omega ( X _ {1} \dots X _ {s} may be regarded as coordinate variables in the space \Omega = \mathbf R ^ {s} ). A multivariate distribution is uniquely determined by its distribution function — the function

F ( x _ {1} \dots x _ {s} ) = \ {\mathsf P} \{ X _ {1} < x _ {1} \dots X _ {s} < x _ {s} \}

of the real variables x _ {1} \dots x _ {s} .

As in the one-dimensional case, the most widespread multivariate distributions are the discrete and the absolutely-continuous distributions. In the discrete case a multivariate distribution is concentrated on a finite or countable set of points ( x _ {i _ {1} } \dots x _ {i _ {s} } ) of \mathbf R ^ {s} such that

{\mathsf P} \{ X _ {1} = x _ {i _ {1} } \dots X _ {s} = x _ {i _ {s} } \} = \ p _ {i _ {1} \dots i _ {s} } \geq 0 ,

\sum _ {i _ {1} \dots i _ {s} } p _ {i _ {1} \dots i _ {s} } = 1

(see, for example, Multinomial distribution). In the absolutely-continuous case almost-everywhere (with respect to Lebesgue measure) on \mathbf R ^ {s} ,

\frac{\partial ^ {s} F ( x _ {1} \dots x _ {s} ) }{\partial x _ {1} \dots \partial x _ {s} } = p ( x _ {1} \dots x _ {s} ) ,

where p ( x _ {1} \dots x _ {s} ) \geq 0 is the density of the multivariate distribution:

{\mathsf P} \{ X \in A \} = \ \int\limits _ { A } p ( x _ {1} \dots x _ {s} ) \ d x _ {1} \dots d x _ {s} ,

for any A from the \sigma - algebra of Borel subsets of \mathbf R ^ {s} , and

\int\limits _ {\mathbf R ^ {s} } p ( x _ {1} \dots x _ {s} ) \ d x _ {1} \dots d x _ {s} = 1 .

The distribution of any random variable X _ {i} ( and also, for any m < s , the distribution of the variables X _ {i _ {1} } \dots X _ {i _ {m} } ) relative to a multivariate distribution is called a marginal distribution. The marginal distributions are completely determined by the given multivariate distribution. When X _ {1} \dots X _ {s} are independent, then

F ( x _ {1} \dots x _ {s} ) = \ F _ {1} ( x _ {1} ) \dots F _ {s} ( x _ {s} )

and

p ( x _ {1} \dots x _ {s} ) = \ p _ {1} ( x _ {1} ) \dots p _ {s} ( x _ {s} ) ,

where F _ {i} ( x) and p _ {i} ( x) are, respectively, the marginal distribution functions and densities of the X _ {i} .

The mathematical expectation of any function f ( X _ {1} \dots X _ {s} ) of X _ {1} \dots X _ {s} is defined by the integral of this function with respect to the multivariate distribution; in particular, in the absolutely-continuous case it is defined by the integral

{\mathsf E} f ( X _ {1} \dots X _ {s} ) =

= \ \int\limits _ {\mathbf R ^ {s} } f ( x _ {1} \dots x _ {s} ) p ( x _ {1} \dots x _ {s} ) d x _ {1} \dots d x _ {s} .

The characteristic function of a multivariate distribution is the function of t = ( t _ {1} \dots t _ {s} ) given by

\phi ( t) = {\mathsf E} e ^ {i t x } ^ \prime ,

where t x ^ \prime = t _ {1} x _ {1} + \dots + t _ {s} x _ {s} . The fundamental characteristics of a multivariate distribution are the moments (cf. Moment): the mixed moments {\mathsf E} X _ {1} ^ {k _ {1} } \dots X _ {s} ^ {k _ {s} } and the central mixed moments {\mathsf E} ( X _ {1} - {\mathsf E} X _ {1} ) ^ {k _ {1} } \dots ( X _ {s} - {\mathsf E} X _ {s} ) ^ {k _ {s} } , where k _ {1} + \dots + k _ {s} is the order of the corresponding moment. The roles of the expectation and the variance for a multivariate distribution are played by {\mathsf E} X = ( {\mathsf E} X _ {1} \dots {\mathsf E} X _ {s} ) and the set of second-order central mixed moments, which form the covariance matrix. If {\mathsf E} ( X _ {i} - {\mathsf E} X _ {i} ) ( X _ {j} - {\mathsf E} X _ {j} ) = 0 for all i , j , i \neq j , then X _ {1} \dots X _ {s} are called pairwise uncorrelated or orthogonal (the covariance matrix is diagonal). If the rank r of the covariance matrix is less than s , then the multivariate distribution is called a degenerate distribution; in this case the distribution is concentrated on some linear manifold in \mathbf R ^ {s} of dimension r < n .

For methods of investigating dependencies between X _ {1} \dots X _ {s} see Correlation; Regression.


References

[a1] N.L. Johnson, S. Kotz, "Discrete distributions" , Houghton Mifflin (1969)
[a2] N.L. Johnson, S. Kotz, "Continuous multivariate distributions" , Wiley (1942)
How to Cite This Entry:
Multi-dimensional distribution. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Multi-dimensional_distribution&oldid=55918
This article was adapted from an original article by A.V. Prokhorov (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article