Complex random vector

In probability theory and statistics, a complex random vector is typically a tuple of complex-valued random variables, and generally is a random variable taking values in a vector space over the field of complex numbers. If ${\displaystyle Z_{1},\ldots ,Z_{n}}$ are complex-valued random variables, then the n-tuple ${\displaystyle \left(Z_{1},\ldots ,Z_{n}\right)}$ is a complex random vector. Complex random variables can always be considered as pairs of real random vectors: their real and imaginary parts.

Some concepts of real random vectors have a straightforward generalization to complex random vectors. For example, the definition of the mean of a complex random vector. Other concepts are unique to complex random vectors.

Applications of complex random vectors are found in digital signal processing.

Part of a series on statistics
Probability theory
Probability Axioms Determinism System Indeterminism Randomness
Probability space Sample space Event Collectively exhaustive events Elementary event Mutual exclusivity Outcome Singleton Experiment Bernoulli trial Probability distribution Bernoulli distribution Binomial distribution Exponential distribution Normal distribution Pareto distribution Poisson distribution Probability measure Random variable Bernoulli process Continuous or discrete Expected value Variance Markov chain Observed value Random walk Stochastic process
Complementary event Joint probability Marginal probability Conditional probability
Independence Conditional independence Law of total probability Law of large numbers Bayes' theorem Boole's inequality
Venn diagram Tree diagram
v t e

Definition

A complex random vector ${\displaystyle \mathbf {Z} =(Z_{1},\ldots ,Z_{n})^{T}}$ on the probability space ${\displaystyle (\Omega ,{\mathcal {F}},P)}$ is a function ${\displaystyle \mathbf {Z} \colon \Omega \rightarrow \mathbb {C} ^{n}}$ such that the vector ${\displaystyle (\Re {(Z_{1})},\Im {(Z_{1})},\ldots ,\Re {(Z_{n})},\Im {(Z_{n})})^{T}}$ is a real random vector on ${\displaystyle (\Omega ,{\mathcal {F}},P)}$ where ${\displaystyle \Re {(z)}}$ denotes the real part of ${\displaystyle z}$ and ${\displaystyle \Im {(z)}}$ denotes the imaginary part of ${\displaystyle z}$.^{[1]: p. 292}

Cumulative distribution function

The generalization of the cumulative distribution function from real to complex random variables is not obvious because expressions of the form ${\displaystyle P(Z\leq 1+3i)}$ make no sense. However expressions of the form ${\displaystyle P(\Re {(Z)}\leq 1,\Im {(Z)}\leq 3)}$ make sense. Therefore, the cumulative distribution function ${\displaystyle F_{\mathbf {Z} }:\mathbb {C} ^{n}\mapsto [0,1]}$ of a random vector ${\displaystyle \mathbf {Z} =(Z_{1},...,Z_{n})^{T}}$ is defined as

where ${\displaystyle \mathbf {z} =(z_{1},...,z_{n})^{T}}$.

Expectation

As in the real case the expectation (also called expected value) of a complex random vector is taken component-wise.^{[1]: p. 293}

Covariance matrix and pseudo-covariance matrix

The covariance matrix (also called second central moment) ${\displaystyle \operatorname {K} _{\mathbf {Z} \mathbf {Z} }}$ contains the covariances between all pairs of components. The covariance matrix of an ${\displaystyle n\times 1}$ random vector is an ${\displaystyle n\times n}$ matrix whose ${\displaystyle (i,j)}$^th element is the covariance between the i ^th and the j ^th random variables.^[2]: p.372 Unlike in the case of real random variables, the covariance between two random variables involves the complex conjugate of one of the two. Thus the covariance matrix is a Hermitian matrix.^{[1]: p. 293}

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

The pseudo-covariance matrix (also called relation matrix) is defined replacing Hermitian transposition by transposition in the definition above.

${\displaystyle \operatorname {J} _{\mathbf {Z} \mathbf {Z} }={\begin{bmatrix}\mathrm {E} [(Z_{1}-\operatorname {E} [Z_{1}])(Z_{1}-\operatorname {E} [Z_{1}])]&\mathrm {E} [(Z_{1}-\operatorname {E} [Z_{1}])(Z_{2}-\operatorname {E} [Z_{2}])]&\cdots &\mathrm {E} [(Z_{1}-\operatorname {E} [Z_{1}])(Z_{n}-\operatorname {E} [Z_{n}])]\\\\\mathrm {E} [(Z_{2}-\operatorname {E} [Z_{2}])(Z_{1}-\operatorname {E} [Z_{1}])]&\mathrm {E} [(Z_{2}-\operatorname {E} [Z_{2}])(Z_{2}-\operatorname {E} [Z_{2}])]&\cdots &\mathrm {E} [(Z_{2}-\operatorname {E} [Z_{2}])(Z_{n}-\operatorname {E} [Z_{n}])]\\\\\vdots &\vdots &\ddots &\vdots \\\\\mathrm {E} [(Z_{n}-\operatorname {E} [Z_{n}])(Z_{1}-\operatorname {E} [Z_{1}])]&\mathrm {E} [(Z_{n}-\operatorname {E} [Z_{n}])(Z_{2}-\operatorname {E} [Z_{2}])]&\cdots &\mathrm {E} [(Z_{n}-\operatorname {E} [Z_{n}])(Z_{n}-\operatorname {E} [Z_{n}])]\end{bmatrix}}}$

Properties

The covariance matrix is a hermitian matrix, i.e.^{[1]: p. 293}

${\displaystyle \operatorname {K} _{\mathbf {Z} \mathbf {Z} }^{H}=\operatorname {K} _{\mathbf {Z} \mathbf {Z} }}$.

The pseudo-covariance matrix is a symmetric matrix, i.e.

${\displaystyle \operatorname {J} _{\mathbf {Z} \mathbf {Z} }^{T}=\operatorname {J} _{\mathbf {Z} \mathbf {Z} }}$.

The covariance matrix is a positive semidefinite matrix, i.e.

${\displaystyle \mathbf {a} ^{H}\operatorname {K} _{\mathbf {Z} \mathbf {Z} }\mathbf {a} \geq 0\quad {\text{for all }}\mathbf {a} \in \mathbb {C} ^{n}}$.

Covariance matrices of real and imaginary parts

By decomposing the random vector ${\displaystyle \mathbf {Z} }$ into its real part ${\displaystyle \mathbf {X} =\Re {(\mathbf {Z} )}}$ and imaginary part ${\displaystyle \mathbf {Y} =\Im {(\mathbf {Z} )}}$ (i.e. ${\displaystyle \mathbf {Z} =\mathbf {X} +i\mathbf {Y} }$), the pair ${\displaystyle (\mathbf {X} ,\mathbf {Y} )}$ has a covariance matrix of the form:

${\displaystyle {\begin{bmatrix}\operatorname {K} _{\mathbf {X} \mathbf {X} }&\operatorname {K} _{\mathbf {Y} \mathbf {X} }\\\operatorname {K} _{\mathbf {X} \mathbf {Y} }&\operatorname {K} _{\mathbf {Y} \mathbf {Y} }\end{bmatrix}}}$

The matrices ${\displaystyle \operatorname {K} _{\mathbf {Z} \mathbf {Z} }}$ and ${\displaystyle \operatorname {J} _{\mathbf {Z} \mathbf {Z} }}$ can be related to the covariance matrices of ${\displaystyle \mathbf {X} }$ and ${\displaystyle \mathbf {Y} }$ via the following expressions:

${\displaystyle {\begin{aligned}&\operatorname {K} _{\mathbf {X} \mathbf {X} }=\operatorname {E} [(\mathbf {X} -\operatorname {E} [\mathbf {X} ])(\mathbf {X} -\operatorname {E} [\mathbf {X} ])^{\mathrm {T} }]={\tfrac {1}{2}}\operatorname {Re} (\operatorname {K} _{\mathbf {Z} \mathbf {Z} }+\operatorname {J} _{\mathbf {Z} \mathbf {Z} })\\&\operatorname {K} _{\mathbf {Y} \mathbf {Y} }=\operatorname {E} [(\mathbf {Y} -\operatorname {E} [\mathbf {Y} ])(\mathbf {Y} -\operatorname {E} [\mathbf {Y} ])^{\mathrm {T} }]={\tfrac {1}{2}}\operatorname {Re} (\operatorname {K} _{\mathbf {Z} \mathbf {Z} }-\operatorname {J} _{\mathbf {Z} \mathbf {Z} })\\&\operatorname {K} _{\mathbf {Y} \mathbf {X} }=\operatorname {E} [(\mathbf {Y} -\operatorname {E} [\mathbf {Y} ])(\mathbf {X} -\operatorname {E} [\mathbf {X} ])^{\mathrm {T} }]={\tfrac {1}{2}}\operatorname {Im} (\operatorname {J} _{\mathbf {Z} \mathbf {Z} }+\operatorname {K} _{\mathbf {Z} \mathbf {Z} })\\&\operatorname {K} _{\mathbf {X} \mathbf {Y} }=\operatorname {E} [(\mathbf {X} -\operatorname {E} [\mathbf {X} ])(\mathbf {Y} -\operatorname {E} [\mathbf {Y} ])^{\mathrm {T} }]={\tfrac {1}{2}}\operatorname {Im} (\operatorname {J} _{\mathbf {Z} \mathbf {Z} }-\operatorname {K} _{\mathbf {Z} \mathbf {Z} })\\\end{aligned}}}$

Conversely:

${\displaystyle {\begin{aligned}&\operatorname {K} _{\mathbf {Z} \mathbf {Z} }=\operatorname {K} _{\mathbf {X} \mathbf {X} }+\operatorname {K} _{\mathbf {Y} \mathbf {Y} }+i(\operatorname {K} _{\mathbf {Y} \mathbf {X} }-\operatorname {K} _{\mathbf {X} \mathbf {Y} })\\&\operatorname {J} _{\mathbf {Z} \mathbf {Z} }=\operatorname {K} _{\mathbf {X} \mathbf {X} }-\operatorname {K} _{\mathbf {Y} \mathbf {Y} }+i(\operatorname {K} _{\mathbf {Y} \mathbf {X} }+\operatorname {K} _{\mathbf {X} \mathbf {Y} })\end{aligned}}}$

Cross-covariance matrix and pseudo-cross-covariance matrix

The cross-covariance matrix between two complex random vectors ${\displaystyle \mathbf {Z} ,\mathbf {W} }$ is defined as:

${\displaystyle \operatorname {K} _{\mathbf {Z} \mathbf {W} }={\begin{bmatrix}\mathrm {E} [(Z_{1}-\operatorname {E} [Z_{1}]){\overline {(W_{1}-\operatorname {E} [W_{1}])}}]&\mathrm {E} [(Z_{1}-\operatorname {E} [Z_{1}]){\overline {(W_{2}-\operatorname {E} [W_{2}])}}]&\cdots &\mathrm {E} [(Z_{1}-\operatorname {E} [Z_{1}]){\overline {(W_{n}-\operatorname {E} [W_{n}])}}]\\\\\mathrm {E} [(Z_{2}-\operatorname {E} [Z_{2}]){\overline {(W_{1}-\operatorname {E} [W_{1}])}}]&\mathrm {E} [(Z_{2}-\operatorname {E} [Z_{2}]){\overline {(W_{2}-\operatorname {E} [W_{2}])}}]&\cdots &\mathrm {E} [(Z_{2}-\operatorname {E} [Z_{2}]){\overline {(W_{n}-\operatorname {E} [W_{n}])}}]\\\\\vdots &\vdots &\ddots &\vdots \\\\\mathrm {E} [(Z_{n}-\operatorname {E} [Z_{n}]){\overline {(W_{1}-\operatorname {E} [W_{1}])}}]&\mathrm {E} [(Z_{n}-\operatorname {E} [Z_{n}]){\overline {(W_{2}-\operatorname {E} [W_{2}])}}]&\cdots &\mathrm {E} [(Z_{n}-\operatorname {E} [Z_{n}]){\overline {(W_{n}-\operatorname {E} [W_{n}])}}]\end{bmatrix}}}$

And the pseudo-cross-covariance matrix is defined as:

${\displaystyle \operatorname {J} _{\mathbf {Z} \mathbf {W} }={\begin{bmatrix}\mathrm {E} [(Z_{1}-\operatorname {E} [Z_{1}])(W_{1}-\operatorname {E} [W_{1}])]&\mathrm {E} [(Z_{1}-\operatorname {E} [Z_{1}])(W_{2}-\operatorname {E} [W_{2}])]&\cdots &\mathrm {E} [(Z_{1}-\operatorname {E} [Z_{1}])(W_{n}-\operatorname {E} [W_{n}])]\\\\\mathrm {E} [(Z_{2}-\operatorname {E} [Z_{2}])(W_{1}-\operatorname {E} [W_{1}])]&\mathrm {E} [(Z_{2}-\operatorname {E} [Z_{2}])(W_{2}-\operatorname {E} [W_{2}])]&\cdots &\mathrm {E} [(Z_{2}-\operatorname {E} [Z_{2}])(W_{n}-\operatorname {E} [W_{n}])]\\\\\vdots &\vdots &\ddots &\vdots \\\\\mathrm {E} [(Z_{n}-\operatorname {E} [Z_{n}])(W_{1}-\operatorname {E} [W_{1}])]&\mathrm {E} [(Z_{n}-\operatorname {E} [Z_{n}])(W_{2}-\operatorname {E} [W_{2}])]&\cdots &\mathrm {E} [(Z_{n}-\operatorname {E} [Z_{n}])(W_{n}-\operatorname {E} [W_{n}])]\end{bmatrix}}}$

Two complex random vectors ${\displaystyle \mathbf {Z} }$ and ${\displaystyle \mathbf {W} }$ are called uncorrelated if

${\displaystyle \operatorname {K} _{\mathbf {Z} \mathbf {W} }=\operatorname {J} _{\mathbf {Z} \mathbf {W} }=0}$.

Independence

Two complex random vectors ${\displaystyle \mathbf {Z} =(Z_{1},...,Z_{m})^{T}}$ and ${\displaystyle \mathbf {W} =(W_{1},...,W_{n})^{T}}$ are called independent if

where ${\displaystyle F_{\mathbf {Z} }(\mathbf {z} )}$ and ${\displaystyle F_{\mathbf {W} }(\mathbf {w} )}$ denote the cumulative distribution functions of ${\displaystyle \mathbf {Z} }$ and ${\displaystyle \mathbf {W} }$ as defined in Eq.1 and ${\displaystyle F_{\mathbf {Z,W} }(\mathbf {z,w} )}$ denotes their joint cumulative distribution function. Independence of ${\displaystyle \mathbf {Z} }$ and ${\displaystyle \mathbf {W} }$ is often denoted by ${\displaystyle \mathbf {Z} \perp \!\!\!\perp \mathbf {W} }$. Written component-wise, ${\displaystyle \mathbf {Z} }$ and ${\displaystyle \mathbf {W} }$ are called independent if

${\displaystyle F_{Z_{1},\ldots ,Z_{m},W_{1},\ldots ,W_{n}}(z_{1},\ldots ,z_{m},w_{1},\ldots ,w_{n})=F_{Z_{1},\ldots ,Z_{m}}(z_{1},\ldots ,z_{m})\cdot F_{W_{1},\ldots ,W_{n}}(w_{1},\ldots ,w_{n})\quad {\text{for all }}z_{1},\ldots ,z_{m},w_{1},\ldots ,w_{n}}$.

Circular symmetry

A complex random vector ${\displaystyle \mathbf {Z} }$ is called circularly symmetric if for every deterministic ${\displaystyle \varphi \in [-\pi ,\pi )}$ the distribution of ${\displaystyle e^{\mathrm {i} \varphi }\mathbf {Z} }$ equals the distribution of ${\displaystyle \mathbf {Z} }$.^{[3]: pp. 500–501}

Properties

• The expectation of a circularly symmetric complex random vector is either zero or it is not defined.^{[3]: p. 500}
• The pseudo-covariance matrix of a circularly symmetric complex random vector is zero.^{[3]: p. 584}

Proper complex random vectors

A complex random vector ${\displaystyle \mathbf {Z} }$ is called proper if the following three conditions are all satisfied:^{[1]: p. 293}

• ${\displaystyle \operatorname {E} [\mathbf {Z} ]=0}$ (zero mean)
• ${\displaystyle \operatorname {var} [Z_{1}]<\infty ,\ldots ,\operatorname {var} [Z_{n}]<\infty }$ (all components have finite variance)
• ${\displaystyle \operatorname {E} [\mathbf {Z} \mathbf {Z} ^{T}]=0}$

Two complex random vectors ${\displaystyle \mathbf {Z} ,\mathbf {W} }$ are called jointly proper is the composite random vector ${\displaystyle (Z_{1},Z_{2},\ldots ,Z_{m},W_{1},W_{2},\ldots ,W_{n})^{T}}$ is proper.

Properties

• A complex random vector ${\displaystyle \mathbf {Z} }$ is proper if, and only if, for all (deterministic) vectors ${\displaystyle \mathbf {c} \in \mathbb {C} ^{n}}$ the complex random variable ${\displaystyle \mathbf {c} ^{T}\mathbf {Z} }$ is proper.^{[1]: p. 293}
• Linear transformations of proper complex random vectors are proper, i.e. if ${\displaystyle \mathbf {Z} }$ is a proper random vectors with ${\displaystyle n}$ components and ${\displaystyle A}$ is a deterministic ${\displaystyle m\times n}$ matrix, then the complex random vector ${\displaystyle A\mathbf {Z} }$ is also proper.^{[1]: p. 295}
• Every circularly symmetric complex random vector with finite variance of all its components is proper.^{[1]: p. 295}
• There are proper complex random vectors that are not circularly symmetric.^{[1]: p. 504}
• A real random vector is proper if and only if it is constant.
• Two jointly proper complex random vectors are uncorrelated if and only if their covariance matrix is zero, i.e. if ${\displaystyle \operatorname {K} _{\mathbf {Z} \mathbf {W} }=0}$.

Cauchy-Schwarz inequality

The Cauchy-Schwarz inequality for complex random vectors is

${\displaystyle \left|\operatorname {E} [\mathbf {Z} ^{H}\mathbf {W} ]\right|^{2}\leq \operatorname {E} [\mathbf {Z} ^{H}\mathbf {Z} ]\operatorname {E} [|\mathbf {W} ^{H}\mathbf {W} |]}$.

Characteristic function

The characteristic function of a complex random vector ${\displaystyle \mathbf {Z} }$ with ${\displaystyle n}$ components is a function ${\displaystyle \mathbb {C} ^{n}\to \mathbb {C} }$ defined by:^{[1]: p. 295}

${\displaystyle \varphi _{\mathbf {Z} }(\mathbf {\omega } )=\operatorname {E} \left[e^{i\Re {(\mathbf {\omega } ^{H}\mathbf {Z} )}}\right]=\operatorname {E} \left[e^{i(\Re {(\omega _{1})}\Re {(Z_{1})}+\Im {(\omega _{1})}\Im {(Z_{1})}+\cdots +\Re {(\omega _{n})}\Re {(Z_{n})}+\Im {(\omega _{n})}\Im {(Z_{n})})}\right]}$

See also

• Complex normal distribution
• Complex random variable (scalar case)

References