SSR_gprior

These sum-squared-residuals (SSR) arise in the variance (or precision)
term under 1) Zellner's g-prior on the coefficients and a Gamma prior on the
error precision and 2) marginalization over the coefficients.

Monte Carlo sampling algorithms for semiparametric
Bayesian regression analysis. These models feature a nonparametric
(unknown) transformation of the data paired with widely-used
regression models including linear regression, spline regression,
quantile regression, and Gaussian processes. The transformation
enables broader applicability of these key models, including for
real-valued, positive, and compactly-supported data with challenging
distributional features. The samplers prioritize computational
scalability and, for most cases, Monte Carlo (not MCMC) sampling for
greater efficiency. Details of the methods and algorithms are provided
in Kowal and Wu (2024) <doi:10.1080/01621459.2024.2395586>.

Dan Kowal

SeBR

Semiparametric Bayesian Regression Analysis

SSR_gprior function

<dl><dt>y</dt>
<dd>vector of response variables</dd>
<dt>X</dt>
<dd>matrix of covariates; if NULL, return <code>sum(y^2)</code></dd>
<dt>psi</dt>
<dd>prior variance (g-prior)</dd></dl>

Arguments

Compute the sum-squared-residuals term under Zellner's g-prior — SSR_gprior

<dl>

<dt>y</dt>
<dd>vector of response variables</dd>


<dt>X</dt>
<dd>matrix of covariates; if NULL, return <code>sum(y^2)</code></dd>


<dt>psi</dt>
<dd>prior variance (g-prior)</dd>

</dl>

SSR_gprior: Compute the sum-squared-residuals term under Zellner's g-prior

Description

Usage

Value

Arguments