📂Mathematical Statistics

Covariance Matrix

Definition¹

$p$-dimensional random vector $\mathbf{X} = \left( X_{1}, \cdots , X_{p} \right)$ is defined as follows for $\operatorname{Cov} (\mathbf{X})$, which is called a Covariance Matrix.

$$\left( \operatorname{Cov} \left( \mathbf{X} \right) \right)_{ij} := \operatorname{Cov} \left( X_{i} , X_{j} \right)$$

---

• $\operatorname{Cov}$ is covariance.

Explanation

To put the definition in simpler words, it is as follows.

$$\operatorname{Cov} \left( \mathbf{X} \right) := \begin{pmatrix} \operatorname{Var} \left( X_{1} \right) & \operatorname{Cov} \left( X_{1} , X_{2} \right) & \cdots & \operatorname{Cov} \left( X_{1} , X_{p} \right) \\ \operatorname{Cov} \left( X_{2} , X_{1} \right) & \operatorname{Var} \left( X_{2} \right) & \cdots & \operatorname{Cov} \left( X_{2} , X_{p} \right) \\ \vdots & \vdots & \ddots & \vdots \\ \operatorname{Cov} \left( X_{p} , X_{1} \right) & \operatorname{Cov} \left( X_{p} , X_{2} \right) & \cdots & \operatorname{Var} \left( X_{p} \right) \end{pmatrix}$$

All covariance matrices are positive semi-definite matrices. In other words, for all vectors $\mathbf{x} \in \mathbb{R}^{p}$, the following holds true.

$$0 \le \textbf{x}^{T} \operatorname{Cov} \left( \mathbf{X} \right) \textbf{x}$$

Theorems

• [1]: If $\mathbf{\mu} \in \mathbb{R}^{p}$ is given as $\mathbf{\mu} := \left( EX_{1} , \cdots , EX_{p} \right)$, $$\operatorname{Cov} (\mathbf{X}) = E \left[ \mathbf{X} \mathbf{X}^{T} \right] - \mathbf{\mu} \mathbf{\mu}^{T}$$
• [2]: If a matrix of constants $A \in \mathbb{R}^{k \times p}$ is given as $(A)_{ij} := a_{ij}$, $$\operatorname{Cov} ( A \mathbf{X}) = A \operatorname{Cov} \left( \mathbf{X} \right) A^{T}$$

---

• $A^{T}$ is the transpose of $A$.

Proof

[1]

$$\begin{align*} \operatorname{Cov} \left( \mathbf{X} \right) =& E \left[ \left( \mathbf{X} - \mathbf{\mu} \right) \left( \mathbf{X} - \mathbf{\mu} \right)^{T} \right] \\ =& E \left[ \mathbf{X} \mathbf{X}^{T} - \mathbf{\mu} \mathbf{X}^{T} - \mathbf{X} \mathbf{\mu}^{T} + \mathbf{\mu} \mathbf{\mu}^{T} \right] \\ =& E \left[ \mathbf{X} \mathbf{X}^{T} \right] - \mathbf{\mu} E \left[ \mathbf{X}^{T} \right] - E \left[ \mathbf{X} \right] \mathbf{\mu}^{T} + E \left[ \mathbf{\mu} \mathbf{\mu}^{T} \right] \\ =& E \left[ \mathbf{X} \mathbf{X}^{T} \right] - \mathbf{\mu} \mathbf{\mu}^{T} \end{align*}$$

■

[2] ²

$$\begin{align*} \operatorname{Cov} \left( A \mathbf{X} \right) =& E \left[ \left( A\mathbf{X} - A\mathbf{\mu} \right) \left( A\mathbf{X} - A\mathbf{\mu} \right)^{T} \right] \\ =& E \left[ A\left(\mathbf{X} -\mathbf{\mu} \right) \left( \mathbf{X} - \mathbf{\mu} \right)^{T} A^{T} \right] \\ =& A E \left[ \left(\mathbf{X} -\mathbf{\mu} \right) \left( \mathbf{X} - \mathbf{\mu} \right)^{T}\right] A^{T} \\ =& A \operatorname{Cov}\left( \mathbf{X} \right) A^{T} \end{align*}$$

■