Covariance matrix

Part of a series on Statistics
Correlation and covariance
Correlation and covariance of random vectors Autocorrelation matrix Cross-correlation matrix Auto-covariance matrix Cross-covariance matrix
Correlation and covariance of stochastic processes Autocorrelation function Cross-correlation function Autocovariance function Cross-covariance function
Correlation and covariance of deterministic signals Autocorrelation function Cross-correlation function Autocovariance function Cross-covariance function

Not to be confused with Cross-covariance matrix.

A bivariate Gaussian probability density function centered at (0, 0), with covariance matrix given by ${\begin{bmatrix}1&0.5\\0.5&1\end{bmatrix}}$

Sample points from a bivariate Gaussian distribution with a standard deviation of 3 in roughly the lower left-upper right direction and of 1 in the orthogonal direction. Because the x and y components co-vary, the variances of $x$ and $y$ do not fully describe the distribution. A $2\times 2$ covariance matrix is needed; the directions of the arrows correspond to the eigenvectors of this covariance matrix and their lengths to the square roots of the eigenvalues.

In probability theory and statistics, a covariance matrix, also known as auto-covariance matrix, dispersion matrix, variance 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 variance to multiple dimensions. As an example, the variation in a collection of random points in two-dimensional space cannot be characterized fully by a single number, nor would the variances in the $x$ and $y$ directions contain all of the necessary information; a $2\times 2$ matrix would be necessary to fully characterize the two-dimensional variation.

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 the variance of one of the random variables. Because the covariance of the i-th random variable with the j-th one is the same thing as the covariance of the j-th random variable with the i-th random variable, every covariance matrix is symmetric. Also, every covariance matrix is positive semi-definite.

The auto-covariance matrix of a random vector $\mathbf {X}$ is typically denoted by $\operatorname {K} _{\mathbf {X} \mathbf {X} }$ or $\Sigma$.

Definition

Throughout this article, boldfaced unsubscripted $\mathbf {X}$ and $\mathbf {Y}$ are used to refer to random vectors, and unboldfaced subscripted $X_{i}$ and $Y_{i}$ are used to refer to scalar random variables.

If the entries in the column vector

$\mathbf {X} =(X_{1},X_{2},...,X_{n})^{\mathrm {T} }$

are random variables, each with finite variance and expected value, then the covariance matrix $\operatorname {K} _{\mathbf {X} \mathbf {X} }$ is the matrix whose $(i,j)$ entry is the covariance

$\operatorname {K} _{X_{i}X_{j}}=\operatorname {cov} [X_{i},X_{j}]=\operatorname {E} [(X_{i}-\operatorname {E} [X_{i}])(X_{j}-\operatorname {E} [X_{j}])]$

where the operator $\operatorname {E}$ denotes the expected value (mean) of its argument.

In other words,

$\operatorname {K} _{\mathbf {X} \mathbf {X} }={\begin{bmatrix}\mathrm {E} [(X_{1}-\operatorname {E} [X_{1}])(X_{1}-\operatorname {E} [X_{1}])]&\mathrm {E} [(X_{1}-\operatorname {E} [X_{1}])(X_{2}-\operatorname {E} [X_{2}])]&\cdots &\mathrm {E} [(X_{1}-\operatorname {E} [X_{1}])(X_{n}-\operatorname {E} [X_{n}])]\\\\\mathrm {E} [(X_{2}-\operatorname {E} [X_{2}])(X_{1}-\operatorname {E} [X_{1}])]&\mathrm {E} [(X_{2}-\operatorname {E} [X_{2}])(X_{2}-\operatorname {E} [X_{2}])]&\cdots &\mathrm {E} [(X_{2}-\operatorname {E} [X_{2}])(X_{n}-\operatorname {E} [X_{n}])]\\\\\vdots &\vdots &\ddots &\vdots \\\\\mathrm {E} [(X_{n}-\operatorname {E} [X_{n}])(X_{1}-\operatorname {E} [X_{1}])]&\mathrm {E} [(X_{n}-\operatorname {E} [X_{n}])(X_{2}-\operatorname {E} [X_{2}])]&\cdots &\mathrm {E} [(X_{n}-\operatorname {E} [X_{n}])(X_{n}-\operatorname {E} [X_{n}])]\end{bmatrix}}$

The definition above is equivalent to the matrix equality

$\operatorname {K} _{\mathbf {X} \mathbf {X} }=\operatorname {cov} [\mathbf {X} ,\mathbf {X} ]=\operatorname {E} [(\mathbf {X} -\mathbf {\mu _{X}} )(\mathbf {X} -\mathbf {\mu _{X}} )^{\rm {T}}]=\operatorname {E} [\mathbf {X} \mathbf {X} ^{T}]-\mathbf {\mu _{X}} \mathbf {\mu _{X}} ^{T}$ (Eq.1)

where $\mathbf {\mu _{X}} =\operatorname {E} [\mathbf {X} ]$.

Generalization of the variance

This form (Eq.1) can be seen as a generalization of the scalar-valued variance to higher dimensions. Recall that for a scalar-valued random variable $X$

$\sigma _{X}^{2}=\operatorname {var} (X)=\operatorname {E} [(X-\operatorname {E} [X])^{2}]=\operatorname {E} [(X-\operatorname {E} [X])\cdot (X-\operatorname {E} [X])].$

Indeed, the entries on the diagonal of the auto-covariance matrix $\operatorname {K} _{\mathbf {X} \mathbf {X} }$ are the variances of each element of the vector $\mathbf {X}$.

Conflicting nomenclatures and notations

Nomenclatures differ. Some statisticians, following the probabilist William Feller in his two-volume book An Introduction to Probability Theory and Its Applications, call the matrix $\operatorname {K} _{\mathbf {X} \mathbf {X} }$ the variance of the random vector $\mathbf {X}$, 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 $\mathbf {X}$.

$\operatorname {var} (\mathbf {X} )=\operatorname {cov} (\mathbf {X} )=\operatorname {E} \left[(\mathbf {X} -\operatorname {E} [\mathbf {X} ])(\mathbf {X} -\operatorname {E} [\mathbf {X} ])^{\rm {T}}\right].$

Both forms are quite standard, and there is no ambiguity between them. The matrix $\operatorname {K} _{\mathbf {X} \mathbf {X} }$ is also often called the variance-covariance matrix, since the diagonal terms are in fact variances.

By comparison, the notation for the cross-covariance between two vectors is

$\operatorname {cov} (\mathbf {X} ,\mathbf {Y} )=\operatorname {K} _{\mathbf {X} \mathbf {Y} }=\operatorname {E} \left[(\mathbf {X} -\operatorname {E} [\mathbf {X} ])(\mathbf {Y} -\operatorname {E} [\mathbf {Y} ])^{\rm {T}}\right].$

Properties

Relation to the correlation matrix

The auto-covariance matrix $\operatorname {K} _{\mathbf {X} \mathbf {X} }$ is related to the autocorrelation matrix $\operatorname {R} _{\mathbf {X} \mathbf {X} }$ by

$\operatorname {K} _{\mathbf {X} \mathbf {X} }=\operatorname {E} [(\mathbf {X} -\operatorname {E} [\mathbf {X} ])(\mathbf {X} -\operatorname {E} [\mathbf {X} ])^{\rm {T}}]=\operatorname {R} _{\mathbf {X} \mathbf {X} }-\operatorname {E} [\mathbf {X} ]\operatorname {E} [\mathbf {X} ]^{\rm {T}}$

where the autocorrelation matrix is defined as $\operatorname {R} _{\mathbf {X} \mathbf {X} }=\operatorname {E} [\mathbf {X} \mathbf {X} ^{\rm {T}}]$.

Relation to the matrix of correlation coefficients

An entity closely related to the covariance matrix is the matrix of Pearson product-moment correlation coefficients between each of the random variables in the random vector $\mathbf {X}$, which can be written as

$\operatorname {corr} (\mathbf {X} )={\big (}\operatorname {diag} (\operatorname {K} _{\mathbf {X} \mathbf {X} }){\big )}^{-{\frac {1}{2}}}\,\operatorname {K} _{\mathbf {X} \mathbf {X} }\,{\big (}\operatorname {diag} (\operatorname {K} _{\mathbf {X} \mathbf {X} }){\big )}^{-{\frac {1}{2}}},$

where $\operatorname {diag} (\operatorname {K} _{\mathbf {X} \mathbf {X} })$ is the matrix of the diagonal elements of $\operatorname {K} _{\mathbf {X} \mathbf {X} }$ (i.e., a diagonal matrix of the variances of $X_{i}$ for $i=1,\dots ,n$).

Equivalently, the correlation matrix can be seen as the covariance matrix of the standardized random variables $X_{i}/\sigma (X_{i})$ for $i=1,\dots ,n$.

$\operatorname {corr} (\mathbf {X} )={\begin{bmatrix}1&{\frac {\operatorname {E} [(X_{1}-\mu _{1})(X_{2}-\mu _{2})]}{\sigma (X_{1})\sigma (X_{2})}}&\cdots &{\frac {\operatorname {E} [(X_{1}-\mu _{1})(X_{n}-\mu _{n})]}{\sigma (X_{1})\sigma (X_{n})}}\\\\{\frac {\operatorname {E} [(X_{2}-\mu _{2})(X_{1}-\mu _{1})]}{\sigma (X_{2})\sigma (X_{1})}}&1&\cdots &{\frac {\operatorname {E} [(X_{2}-\mu _{2})(X_{n}-\mu _{n})]}{\sigma (X_{2})\sigma (X_{n})}}\\\\\vdots &\vdots &\ddots &\vdots \\\\{\frac {\operatorname {E} [(X_{n}-\mu _{n})(X_{1}-\mu _{1})]}{\sigma (X_{n})\sigma (X_{1})}}&{\frac {\operatorname {E} [(X_{n}-\mu _{n})(X_{2}-\mu _{2})]}{\sigma (X_{n})\sigma (X_{2})}}&\cdots &1\end{bmatrix}}.$

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.

Inverse of the covariance matrix

The inverse of this matrix, $\operatorname {K} _{\mathbf {X} \mathbf {X} }^{-1}$, if it exists, is the inverse covariance matrix, also known as the concentration matrix or precision matrix.

Basic properties

For $\operatorname {K} _{\mathbf {X} \mathbf {X} }=\operatorname {var} (\mathbf {X} )=\operatorname {E} \left[\left(\mathbf {X} -\operatorname {E} [\mathbf {X} ]\right)\left(\mathbf {X} -\operatorname {E} [\mathbf {X} ]\right)^{\rm {T}}\right]$ and $\mathbf {\mu _{X}} =\operatorname {E} ({\textbf {X}})$, where $\mathbf {X} =(X_{1},\ldots ,X_{n})$ is a $n$-dimensional random variable, the following basic properties apply:

1. $\operatorname {K} _{\mathbf {X} \mathbf {X} }=\operatorname {E} (\mathbf {XX^{\rm {T}}} )-\mathbf {\mu _{X}} \mathbf {\mu _{X}} ^{\rm {T}}$
2. $\operatorname {K} _{\mathbf {X} \mathbf {X} }\,$ is positive-semidefinite, i.e. $\mathbf {a} ^{T}\Sigma \mathbf {a} \geq 0\quad {\text{for all }}\mathbf {a} \in \mathbb {R} ^{n}$
3. $\operatorname {K} _{\mathbf {X} \mathbf {X} }\,$ is symmetric, i.e. $\operatorname {K} _{\mathbf {X} \mathbf {X} }^{\rm {T}}=\operatorname {K} _{\mathbf {X} \mathbf {X} }$
4. For any constant (i.e. non-random) $m\times n$ matrix $\mathbf {A}$ and constant $m\times 1$ vector $\mathbf {a}$, one has $\operatorname {var} (\mathbf {AX} +\mathbf {a} )=\mathbf {A} \,\operatorname {var} (\mathbf {X} )\,\mathbf {A} ^{\rm {T}}$
5. If $\mathbf {Y}$ is another random vector with the same dimension as $\mathbf {X}$, then $\operatorname {var} (\mathbf {X} +\mathbf {Y} )=\operatorname {var} (\mathbf {X} )+\operatorname {cov} (\mathbf {X} ,\mathbf {Y} )+\operatorname {cov} (\mathbf {Y} ,\mathbf {X} )+\operatorname {var} (\mathbf {Y} )$ where $\operatorname {cov} (\mathbf {X} ,\mathbf {Y} )$ is the cross-covariance matrix of $\mathbf {X}$ and $\mathbf {Y}$.

Block matrices

The joint mean $\mathbf {\mu } _{X,Y}$ and joint covariance matrix ${\boldsymbol {\Sigma }}_{X,Y}$ of $\mathbf {X}$ and $\mathbf {Y}$ can be written in block form

${\boldsymbol {\mu }}_{X,Y}={\begin{bmatrix}{\boldsymbol {\mu }}_{X}\\{\boldsymbol {\mu }}_{Y}\end{bmatrix}},\qquad {\boldsymbol {\Sigma }}_{X,Y}={\begin{bmatrix}{\boldsymbol {\Sigma }}_{\mathit {XX}}&{\boldsymbol {\Sigma }}_{\mathit {XY}}\\{\boldsymbol {\Sigma }}_{\mathit {YX}}&{\boldsymbol {\Sigma }}_{\mathit {YY}}\end{bmatrix}}$

where ${\boldsymbol {\Sigma }}_{XX}=\operatorname {var} ({\boldsymbol {X}}),{\boldsymbol {\Sigma }}_{YY}=\operatorname {var} ({\boldsymbol {Y}}),$ and ${\boldsymbol {\Sigma }}_{XY}={\boldsymbol {\Sigma }}_{\mathit {YX}}^{T}=\operatorname {cov} ({\boldsymbol {X}},{\boldsymbol {Y}})$.

${\boldsymbol {\Sigma }}_{XX}$ and ${\boldsymbol {\Sigma }}_{YY}$ can be identified as the variance matrices of the marginal distributions for ${\boldsymbol {X}}$ and ${\boldsymbol {Y}}$ respectively.

If ${\boldsymbol {X}}$ and ${\boldsymbol {Y}}$ are jointly normally distributed,

${\boldsymbol {X}},{\boldsymbol {Y}}\sim \ {\mathcal {N}}({\boldsymbol {\mu }}_{X,Y},{\boldsymbol {\Sigma }}_{X,Y}),$

then the conditional distribution for ${\boldsymbol {Y}}$ given ${\boldsymbol {X}}$ is given by

${\boldsymbol {Y}}\mid {\boldsymbol {X}}\sim \ {\mathcal {N}}({\boldsymbol {\mu }}_{Y|X},{\boldsymbol {\Sigma }}_{Y\mid X}),$

defined by conditional mean

${\boldsymbol {\mu }}_{Y\mid X}={\boldsymbol {\mu }}_{Y}+{\boldsymbol {\Sigma }}_{YX}{\boldsymbol {\Sigma }}_{XX}^{-1}\left(\mathbf {x} -{\boldsymbol {\mu }}_{X}\right)$

and conditional variance

${\boldsymbol {\Sigma }}_{Y\mid X}={\boldsymbol {\Sigma }}_{YY}-{\boldsymbol {\Sigma }}_{\mathit {YX}}{\boldsymbol {\Sigma }}_{\mathit {XX}}^{-1}{\boldsymbol {\Sigma }}_{\mathit {XY}}.$

The matrix ${\boldsymbol {\Sigma }}_{YX}{\boldsymbol {\Sigma }}_{XX}^{-1}$ is known as the matrix of regression coefficients, while in linear algebra ${\boldsymbol {\Sigma }}_{Y\mid X}$ is the Schur complement of ${\boldsymbol {\Sigma }}_{XX}$ in ${\boldsymbol {\Sigma }}_{X,Y}$.

The matrix of regression coefficients may often be given in transpose form, ${\boldsymbol {\Sigma }}_{XX}^{-1}{\boldsymbol {\Sigma }}_{XY}$, suitable for post-multiplying a row vector of explanatory variables x^T 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).

Covariance matrix 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.

Covariance matrix as a linear operator

Main article: Covariance 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: $\mathbf {c} ^{\rm {T}}\Sigma =\operatorname {cov} (\mathbf {c} ^{\rm {T}}\mathbf {X} ,\mathbf {X} )$. Treated as a bilinear form, it yields the covariance between the two linear combinations: $\mathbf {d} ^{\rm {T}}\Sigma \mathbf {c} =\operatorname {cov} (\mathbf {d} ^{\rm {T}}\mathbf {X} ,\mathbf {c} ^{\rm {T}}\mathbf {X} )$. The variance of a linear combination is then $\mathbf {c} ^{\rm {T}}\Sigma \mathbf {c}$, its covariance with itself.

Similarly, the (pseudo-)inverse covariance matrix provides an inner product $\langle c-\mu |\Sigma ^{+}|c-\mu \rangle$, which induces the Mahalanobis distance, a measure of the "unlikelihood" of c.

Which matrices are covariance matrices?

From the identity just above, let $\mathbf {b}$ be a $(p\times 1)$ real-valued vector, then

$\operatorname {var} (\mathbf {b} ^{\rm {T}}\mathbf {X} )=\mathbf {b} ^{\rm {T}}\operatorname {var} (\mathbf {X} )\mathbf {b} ,\,$

which must always be nonnegative, since it is the variance of a real-valued random variable. A covariance matrix is always a positive-semidefinite matrix, since

${\begin{aligned}&w^{\rm {T}}\operatorname {E} \left[(\mathbf {X} -\operatorname {E} [\mathbf {X} ])(\mathbf {X} -\operatorname {E} [\mathbf {X} ])^{\rm {T}}\right]w=\operatorname {E} \left[w^{\rm {T}}(\mathbf {X} -\operatorname {E} [\mathbf {X} ])(\mathbf {X} -\operatorname {E} [\mathbf {X} ])^{\rm {T}}w\right]\\[5pt]={}&\operatorname {E} {\big [}{\big (}w^{\rm {T}}(\mathbf {X} -\operatorname {E} [\mathbf {X} ]){\big )}^{2}{\big ]}\geq 0\quad {\text{since }}w^{\rm {T}}(\mathbf {X} -\operatorname {E} [\mathbf {X} ]){\text{ is a scalar}}.\end{aligned}}$

Conversely, every symmetric positive semi-definite matrix is a covariance matrix. To see this, suppose $M$ is a $p\times p$ positive-semidefinite matrix. From the finite-dimensional case of the spectral theorem, it follows that $M$ has a nonnegative symmetric square root, which can be denoted by M^1/2. Let $\mathbf {X}$ be any $p\times 1$ column vector-valued random variable whose covariance matrix is the $p\times p$ identity matrix. Then

$\operatorname {var} (\mathbf {M} ^{1/2}\mathbf {X} )=\mathbf {M} ^{1/2}\,\operatorname {var} (\mathbf {X} )\,\mathbf {M} ^{1/2}=\mathbf {M} .$

Complex random vectors

Main article: Complex random vector

Covariance matrix

The variance of a complex scalar-valued random variable with expected value $\mu$ is conventionally defined using complex conjugation:

$\operatorname {var} (Z)=\operatorname {E} \left[(Z-\mu _{Z}){\overline {(Z-\mu _{Z})}}\right],$

where the complex conjugate of a complex number $z$ is denoted ${\overline {z}}$; thus the variance of a complex random variable is a real number.

If $\mathbf {Z} =(Z_{1},\ldots ,Z_{n})^{\mathrm {T} }$ 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 called the covariance matrix, as its expectation:

$\operatorname {K} _{\mathbf {Z} \mathbf {Z} }=\operatorname {cov} [\mathbf {Z} ,\mathbf {Z} ]=\operatorname {E} \left[(\mathbf {Z} -\mathbf {\mu _{Z}} )(\mathbf {Z} -\mathbf {\mu _{Z}} )^{\mathrm {H} }\right]$,

where ${}^{\mathrm {H} }$ 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.

Pseudo-covariance matrix

For complex random vectors, another kind of second central moment, the pseudo-covariance matrix (also called relation matrix) is defined as follows. In contrast to the covariance matrix defined above Hermitian transposition gets replaced by transposition in the definition.

$\operatorname {J} _{\mathbf {Z} \mathbf {Z} }=\operatorname {cov} [\mathbf {Z} ,{\overline {\mathbf {Z} }}]=\operatorname {E} \left[(\mathbf {Z} -\mathbf {\mu _{Z}} )(\mathbf {Z} -\mathbf {\mu _{Z}} )^{\mathrm {T} }\right]$

Properties

• The covariance matrix is a Hermitian matrix, i.e. $\operatorname {K} _{\mathbf {Z} \mathbf {Z} }^{\mathrm {H} }=\operatorname {K} _{\mathbf {Z} \mathbf {Z} }$.
• The diagonal elements of the covariance matrix are real.

Estimation

Main article: Estimation of covariance matrices

If $\mathbf {M} _{\mathbf {X} }$ and $\mathbf {M} _{\mathbf {Y} }$ are centred data matrices of dimension $n\times p$ and $n\times q$ 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 $\mathbf {Q} _{\mathbf {X} }$ and $\mathbf {Q} _{\mathbf {XY} }$ can be defined to be

$\mathbf {Q} _{\mathbf {X} }={\frac {1}{n-1}}\mathbf {M} _{\mathbf {X} }^{\rm {T}}\mathbf {M} _{\mathbf {X} },\qquad \mathbf {Q} _{\mathbf {XY} }={\frac {1}{n-1}}\mathbf {M} _{\mathbf {X} }^{\rm {T}}\mathbf {M} _{\mathbf {Y} }$

or, if the column means were known a priori,

$\mathbf {Q} _{\mathbf {X} }={\frac {1}{n}}\mathbf {M} _{\mathbf {X} }^{\rm {T}}\mathbf {M} _{\mathbf {X} },\qquad \mathbf {Q} _{\mathbf {XY} }={\frac {1}{n}}\mathbf {M} _{\mathbf {X} }^{\rm {T}}\mathbf {M} _{\mathbf {Y} }.$

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.

Applications

The 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 component analysis (PCA) and the Karhunen–Loève transform (KL-transform).

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.

See also

• Multivariate statistics
• Gramian matrix
• Eigenvalue decomposition
• Quadratic form (statistics)
• Principal components

Further reading

• Hazewinkel, Michiel, ed. (2001) [1994], "Covariance matrix", Encyclopedia of Mathematics, Springer Science+Business Media B.V. / Kluwer Academic Publishers, 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.