In multivariate statistics, if ${\displaystyle \varepsilon }$ is a vector of ${\displaystyle n}$ random variables, and ${\displaystyle \Lambda }$ is an ${\displaystyle n}$-dimensional symmetric matrix, then the scalar quantity ${\displaystyle \varepsilon ^{T}\Lambda \varepsilon }$ is known as a quadratic form in ${\displaystyle \varepsilon }$.

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It can be shown that^[1]

${\displaystyle \operatorname {E} \left[\varepsilon ^{T}\Lambda \varepsilon \right]=\operatorname {tr} \left[\Lambda \Sigma \right]+\mu ^{T}\Lambda \mu }$

where ${\displaystyle \mu }$ and ${\displaystyle \Sigma }$ are the expected value and variance-covariance matrix of ${\displaystyle \varepsilon }$, respectively, and tr denotes the trace of a matrix. This result only depends on the existence of ${\displaystyle \mu }$ and ${\displaystyle \Sigma }$; in particular, normality of ${\displaystyle \varepsilon }$ is not required.

A book treatment of the topic of quadratic forms in random variables is that of Mathai and Provost.^[2]

In general, the variance of a quadratic form depends greatly on the distribution of ${\displaystyle \varepsilon }$. However, if ${\displaystyle \varepsilon }$ does follow a multivariate normal distribution, the variance of the quadratic form becomes particularly tractable. Assume for the moment that ${\displaystyle \Lambda }$ is a symmetric matrix. Then,

${\displaystyle \operatorname {var} \left[\varepsilon ^{T}\Lambda \varepsilon \right]=2\operatorname {tr} \left[\Lambda \Sigma \Lambda \Sigma \right]+4\mu ^{T}\Lambda \Sigma \Lambda \mu }$.^[3]

In fact, this can be generalized to find the covariance between two quadratic forms on the same ${\displaystyle \varepsilon }$ (once again, ${\displaystyle \Lambda _{1}}$ and ${\displaystyle \Lambda _{2}}$ must both be symmetric):

${\displaystyle \operatorname {cov} \left[\varepsilon ^{T}\Lambda _{1}\varepsilon ,\varepsilon ^{T}\Lambda _{2}\varepsilon \right]=2\operatorname {tr} \left[\Lambda _{1}\Sigma \Lambda _{2}\Sigma \right]+4\mu ^{T}\Lambda _{1}\Sigma \Lambda _{2}\mu }$.^[4]

In addition, a quadratic form such as this follows a generalized chi-squared distribution.

In the setting where one has a set of observations ${\displaystyle y}$ and an operator matrix ${\displaystyle H}$, then the residual sum of squares can be written as a quadratic form in ${\displaystyle y}$:

${\displaystyle {\textrm {RSS}}=y^{T}(I-H)^{T}(I-H)y.}$

For procedures where the matrix ${\displaystyle H}$ is symmetric and idempotent, and the errors are Gaussian with covariance matrix ${\displaystyle \sigma ^{2}I}$, ${\displaystyle {\textrm {RSS}}/\sigma ^{2}}$ has a chi-squared distribution with ${\displaystyle k}$ degrees of freedom and noncentrality parameter ${\displaystyle \lambda }$, where

${\displaystyle k=\operatorname {tr} \left[(I-H)^{T}(I-H)\right]}$

${\displaystyle \lambda =\mu ^{T}(I-H)^{T}(I-H)\mu /2}$

may be found by matching the first two central moments of a noncentral chi-squared random variable to the expressions given in the first two sections. If ${\displaystyle Hy}$ estimates ${\displaystyle \mu }$ with no bias, then the noncentrality ${\displaystyle \lambda }$ is zero and ${\displaystyle {\textrm {RSS}}/\sigma ^{2}}$ follows a central chi-squared distribution.

• Quadratic form
• Covariance matrix
• Matrix representation of conic sections