Index: The Book of Statistical ProofsStatistical Models ▷ Univariate normal data ▷ Bayesian linear regression ▷ Posterior credibility region excluding null hypothesis

Theorem: Let there be a linear regression model with normally distributed errors:

\[\label{eq:GLM} y = X \beta + \varepsilon, \; \varepsilon \sim \mathcal{N}(0, \sigma^2 V)\]

and assume a normal-gamma prior distribution over the model parameters $\beta$ and $\tau = 1/\sigma^2$:

\[\label{eq:GLM-NG-prior} p(\beta,\tau) = \mathcal{N}(\beta; \mu_0, (\tau \Lambda_0)^{-1}) \cdot \mathrm{Gam}(\tau; a_0, b_0) \; .\]

Then, the largest posterior credibility region that does not contain the omnibus null hypothesis

\[\label{eq:GLM-H0} \mathrm{H}_0: \, C^\mathrm{T} \beta = 0\]

is given by the credibility level

\[\label{eq:GLM-NG-PCR} (1-\alpha) = \mathrm{F}\left( \left[ \mu^\mathrm{T} C (C^\mathrm{T} \Sigma \, C)^{-1} C^\mathrm{T} \mu \right]/q; q, \nu \right)\]

where $C$ is a $p \times q$ contrast matrix, $\mathrm{F}(x; v, w)$ is the cumulative distribution function of the F-distribution with $v$ numerator degrees of freedom, $w$ denominator degrees of freedom and $\mu$, $\Sigma$ and $\nu$ can be obtained from the posterior hyperparameters of Bayesian linear regression.

Proof: The posterior distribution for Bayesian linear regression is given by a normal-gamma distribution over $\beta$ and $\tau = 1/\sigma^2$

\[\label{eq:GLM-NG-post} p(\beta,\tau|y) = \mathcal{N}(\beta; \mu_n, (\tau \Lambda_n)^{-1}) \cdot \mathrm{Gam}(\tau; a_n, b_n)\]

with the posterior hyperparameters

\[\label{eq:GLM-NG-post-par} \begin{split} \mu_n &= \Lambda_n^{-1} (X^\mathrm{T} P y + \Lambda_0 \mu_0) \\ \Lambda_n &= X^\mathrm{T} P X + \Lambda_0 \\ a_n &= a_0 + \frac{n}{2} \\ b_n &= b_0 + \frac{1}{2} (y^\mathrm{T} P y + \mu_0^\mathrm{T} \Lambda_0 \mu_0 - \mu_n^\mathrm{T} \Lambda_n \mu_n) \; . \end{split}\]

The marginal distribution of a normal-gamma distribution is a multivariate t-distribution, such that the marginal posterior distribution of $\beta$ is

\[\label{eq:GLM-NG-post-beta} p(\beta|y) = t(\beta; \mu, \Sigma, \nu)\]

with the posterior hyperparameters

\[\label{eq:GLM-NG-post-par-beta} \begin{split} \mu &= \mu_n \\ \Sigma &= \left( \frac{a_n}{b_n} \Lambda_n \right)^{-1} \\ \nu &= 2 \, a_n \; . \end{split}\]

Define the quantity $\gamma = C^\mathrm{T} \beta$. According to the linear transformation theorem for the multivariate t-distribution, $\gamma$ also follows a multivariate t-distribution:

\[\label{eq:GLM-NG-post-gamma} p(\gamma|y) = t(\gamma; C^\mathrm{T} \mu, C^\mathrm{T} \Sigma \, C, \nu) \; .\]

Because $C^\mathrm{T}$ is a $q \times p$ matrix, $\gamma$ is a $q \times 1$ vector. The quadratic form of a multivariate t-distributed random variable has an F-distribution, such that we can write:

\[\label{eq:GLM-NG-post-qf} \mathrm{QF}(\gamma) = (\gamma - C^\mathrm{T} \mu)^\mathrm{T} (C^\mathrm{T} \Sigma \, C)^{-1} (\gamma - C^\mathrm{T} \mu) /q \, \sim \mathrm{F}(q,\nu) \; .\]

Therefore, the largest posterior credibility region for $\gamma$ which does not contain $\gamma = 0_q$ (i.e. only touches this origin point) can be obtained by plugging $\mathrm{QF}(0)$ into the cumulative distribution function of the F-distribution:

\[\label{eq:GLM-NG-post-cred-reg-not-H0} \begin{split} (1-\alpha) &= \mathrm{F}\left( \mathrm{QF}(0); q, \nu \right) \\ &= \mathrm{F}\left( \left[ \mu^\mathrm{T} C (C^\mathrm{T} \Sigma \, C)^{-1} C^\mathrm{T} \mu \right]/q; q, \nu \right) \; . \end{split}\]

Metadata: ID: P134 | shortcut: blr-pcr | author: JoramSoch | date: 2020-07-17, 17:41.