Quadratic form (statistics)

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

Expectation

It can be shown that[1]

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

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

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

Proof

Since the quadratic form is a scalar quantity, $\varepsilon ^{T}\Lambda \varepsilon =\operatorname {tr} (\varepsilon ^{T}\Lambda \varepsilon )$ .

Next, by the cyclic property of the trace operator,

\operatorname {E} [\operatorname {tr} (\varepsilon ^{T}\Lambda \varepsilon )]=\operatorname {E} [\operatorname {tr} (\Lambda \varepsilon \varepsilon ^{T})].

Since the trace operator is a linear combination of the components of the matrix, it therefore follows from the linearity of the expectation operator that

\operatorname {E} [\operatorname {tr} (\Lambda \varepsilon \varepsilon ^{T})]=\operatorname {tr} (\Lambda \operatorname {E} (\varepsilon \varepsilon ^{T})).

A standard property of variances then tells us that this is

\operatorname {tr} (\Lambda (\Sigma +\mu \mu ^{T})).

Applying the cyclic property of the trace operator again, we get

\operatorname {tr} (\Lambda \Sigma )+\operatorname {tr} (\Lambda \mu \mu ^{T})=\operatorname {tr} (\Lambda \Sigma )+\operatorname {tr} (\mu ^{T}\Lambda \mu )=\operatorname {tr} (\Lambda \Sigma )+\mu ^{T}\Lambda \mu .

Variance in the Gaussian case

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

\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 $\varepsilon$ (once again, $\Lambda _{1}$ and $\Lambda _{2}$ must both be symmetric):

\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]

Computing the variance in the non-symmetric case

Some texts incorrectly state that the above variance or covariance results hold without requiring $\Lambda$ to be symmetric. The case for general $\Lambda$ can be derived by noting that

\varepsilon ^{T}\Lambda ^{T}\varepsilon =\varepsilon ^{T}\Lambda \varepsilon

so

\varepsilon ^{T}{\tilde {\Lambda }}\varepsilon =\varepsilon ^{T}\left(\Lambda +\Lambda ^{T}\right)\varepsilon /2

is a quadratic form in the symmetric matrix ${\tilde {\Lambda }}=\left(\Lambda +\Lambda ^{T}\right)/2$ , so the mean and variance expressions are the same, provided $\Lambda$ is replaced by ${\tilde {\Lambda }}$ therein.

Examples of quadratic forms

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

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

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

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

\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 $Hy$ estimates $\mu$ with no bias, then the noncentrality $\lambda$ is zero and ${\textrm {RSS}}/\sigma ^{2}$ follows a central chi-squared distribution.

References

Bates, Douglas. "Quadratic Forms of Random Variables" (PDF). STAT 849 lectures. Retrieved August 21, 2011.
Mathai, A. M. & Provost, Serge B. (1992). Quadratic Forms in Random Variables. CRC Press. p. 424. ISBN 978-0824786915.
Rencher, Alvin C.; Schaalje, G. Bruce. (2008). Linear models in statistics (2nd ed.). Hoboken, N.J.: Wiley-Interscience. ISBN 9780471754985. OCLC 212120778.
Graybill, Franklin A. Matrices with applications in statistics (2. ed.). Wadsworth: Belmont, Calif. p. 367. ISBN 0534980384.

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[1] Bates, Douglas. "Quadratic Forms of Random Variables" (PDF). STAT 849 lectures. Retrieved August 21, 2011.

[Mathai-2] Mathai, A. M. & Provost, Serge B. (1992). Quadratic Forms in Random Variables. CRC Press. p. 424. ISBN 978-0824786915.

[3] Rencher, Alvin C.; Schaalje, G. Bruce. (2008). Linear models in statistics (2nd ed.). Hoboken, N.J.: Wiley-Interscience. ISBN 9780471754985. OCLC 212120778.

[4] Graybill, Franklin A. Matrices with applications in statistics (2. ed.). Wadsworth: Belmont, Calif. p. 367. ISBN 0534980384.