g_inv_bc

Evaluate the inverse Box-Cox transformation. Negative values are permitted.

Monte Carlo and MCMC 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 (2023) <arXiv:2306.05498>.

Dan Kowal

SeBR

Semiparametric Bayesian Regression Analysis

g_inv_bc function

<dl><dt>s</dt>
<dd>argument(s) at which to evaluate the function</dd>
<dt>lambda</dt>
<dd>Box-Cox parameter</dd></dl>

Arguments

Inverse Box-Cox transformation — g_inv_bc

<dl>

<dt>s</dt>
<dd>argument(s) at which to evaluate the function</dd>


<dt>lambda</dt>
<dd>Box-Cox parameter</dd>

</dl>

g_inv_bc: Inverse Box-Cox transformation

Description

Usage

Value

Arguments