In probability theory and statistics, a covariance matrix (also known as dispersion matrix or variance–covariance matrix) is a matrix whose element in the i, j position is the covariance between the i th and j th elements of a random vector. A random vector is a random variable with multiple dimensions. Each element of the vector is a scalar random variable. Each element has either a finite number of observed empirical values or a finite or infinite number of potential values. The potential values are specified by a theoretical joint probability distribution.
Intuitively, the covariance matrix generalizes the notion of covariance to multiple dimensions. As an example, let's consider two vectors and . There are four covariances to consider: with , with , with , and with . These variances cannot be summarized in a scalar. Of course, a 2×2 matrix is the most natural choice to describe the covariance: the first row containing the covariances of with and , and the second row containing the covariances of with and .
Because the covariance of the i th random variable with itself is simply that random variable's variance, each element on the principal diagonal of the covariance matrix is just the variance of each of the elements in the vector. Because , every covariance matrix is symmetric. In addition, every covariance matrix is positive semi-definite.
Throughout this article, boldfaced unsubscripted X and Y are used to refer to random vectors, and unboldfaced subscripted Xi and Yi are used to refer to random scalars.
If the entries in the column vector
is the expected value of the i th entry in the vector X. In other words,
The inverse of this matrix, is the inverse covariance matrix, also known as the concentration matrix or precision matrix.
Generalization of the variance
The definition above is equivalent to the matrix equality
This form can be seen as a generalization of the scalar-valued variance to higher dimensions. Recall that for a scalar-valued random variable X
Indeed, the entries on the diagonal of the covariance matrix are the variances of each element of the vector .
A quantity closely related to the covariance matrix is the correlation matrix, the matrix of Pearson product-moment correlation coefficients between each of the random variables in the random vector , which can be written
where is the matrix of the diagonal elements of (i.e., a diagonal matrix of the variances of for ).
Equivalently, the correlation matrix can be seen as the covariance matrix of the standardized random variables for .
Each element on the principal diagonal of a correlation matrix is the correlation of a random variable with itself, which always equals 1. Each off-diagonal element is between 1 and –1 inclusive.
Conflicting nomenclatures and notations
Nomenclatures differ. Some statisticians, following the probabilist William Feller, call the matrix the variance of the random vector , because it is the natural generalization to higher dimensions of the 1-dimensional variance. Others call it the covariance matrix, because it is the matrix of covariances between the scalar components of the vector .
However, the notation for the cross-covariance between two vectors is standard:
The matrix is also often called the variance-covariance matrix since the diagonal terms are in fact variances.
where and are random p×1 vectors, is a random q×1 vector, is a q×1 vector, is a p×1 vector, and and are q×p matrices of constants.
This covariance matrix is a useful tool in many different areas. From it a transformation matrix can be derived, called a whitening transformation, that allows one to completely decorrelate the data or, from a different point of view, to find an optimal basis for representing the data in a compact way (see Rayleigh quotient for a formal proof and additional properties of covariance matrices). This is called principal components analysis (PCA) and the Karhunen-Loève transform (KL-transform).
The joint mean and joint covariance matrix of and can be written in block form
where and .
and can be identified as the variance matrices of the marginal distributions for and respectively.
If and are jointly normally distributed,
then the conditional distribution for given is given by
defined by conditional mean
The matrix of regression coefficients may often be given in transpose form, ΣXX−1ΣXY, suitable for post-multiplying a row vector of explanatory variables xT rather than pre-multiplying a column vector x. In this form they correspond to the coefficients obtained by inverting the matrix of the normal equations of ordinary least squares (OLS).
As a parameter of a distribution
If a vector of n possibly correlated random variables is jointly normally distributed, or more generally elliptically distributed, then its probability density function can be expressed in terms of the covariance matrix.
As a linear operator
Applied to one vector, the covariance matrix maps a linear combination, c, of the random variables, X, onto a vector of covariances with those variables: . Treated as a bilinear form, it yields the covariance between the two linear combinations: . The variance of a linear combination is then , its covariance with itself.
Similarly, the (pseudo-)inverse covariance matrix provides an inner product, which induces the Mahalanobis distance, a measure of the "unlikelihood" of c.
Complex random vectors
where the complex conjugate of a complex number is denoted ; thus the variance of a complex number is a real number.
If is a column-vector of complex-valued random variables, then the conjugate transpose is formed by both transposing and conjugating. In the following expression, the product of a vector with its conjugate transpose results in a square matrix, as its expectation:
where denotes the conjugate transpose, which is applicable to the scalar case since the transpose of a scalar is still a scalar. The matrix so obtained will be Hermitian positive-semidefinite, with real numbers in the main diagonal and complex numbers off-diagonal.
If and are centred data matrices of dimension n-by-p and n-by-q respectively, i.e. with n rows of observations of p and q columns of variables, from which the column means have been subtracted, then, if the column means were estimated from the data, sample correlation matrices and can be defined to be
or, if the column means were known a-priori,
These empirical sample correlation matrices are the most straightforward and most often used estimators for the correlation matrices, but other estimators also exist, including regularised or shrinkage estimators, which may have better properties.
The covariance matrix plays a key role in financial economics, especially in portfolio theory and its mutual fund separation theorem and in the capital asset pricing model. The matrix of covariances among various assets' returns is used to determine, under certain assumptions, the relative amounts of different assets that investors should (in a normative analysis) or are predicted to (in a positive analysis) choose to hold in a context of diversification.
- Covariance mapping
- Multivariate statistics
- Gramian matrix
- Eigenvalue decomposition
- Quadratic form (statistics)
- Principal components
- Wasserman, Larry (2004). All of Statistics: A Concise Course in Statistical Inference. ISBN 0-387-40272-1.
- William Feller (1971). An introduction to probability theory and its applications. Wiley. ISBN 978-0-471-25709-7. Retrieved 10 August 2012.
- Taboga, Marco (2010). "Lectures on probability theory and mathematical statistics".
- Eaton, Morris L. (1983). Multivariate Statistics: a Vector Space Approach. John Wiley and Sons. pp. 116–117. ISBN 0-471-02776-6.
- Brookes, Mike. "The Matrix Reference Manual".
- Hazewinkel, Michiel, ed. (2001), "Covariance matrix", Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4
- Weisstein, Eric W. "Covariance Matrix". MathWorld.
- van Kampen, N. G. (1981). Stochastic processes in physics and chemistry. New York: North-Holland. ISBN 0-444-86200-5.