get_cox_qr_solve

This function solves the linear system defined by <code>hessian_matrix</code> and <code>gradient_vector</code>
using QR decomposition. Any NA values in the resulting solution vector are replaced with 0.0001. If there
is an error during the solution process, a vector of default values (0.0001) is returned instead.

To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

Dongming Huang

catalytic

Tools for Applying Catalytic Priors in Statistical Modeling

Yitong Wu

Weihao Li

Ministry of Education, Singapore 

get_cox_qr_solve function

<dl><dt>hessian_matrix</dt>
<dd>A matrix of coefficients representing the system of linear equations.</dd>
<dt>gradient_vector</dt>
<dd>A numeric vector representing the constants in the linear system.</dd></dl>

Arguments

Solve Linear System using QR Decomposition — get_cox_qr_solve

<dl>

<dt>hessian_matrix</dt>
<dd>A matrix of coefficients representing the system of linear equations.</dd>


<dt>gradient_vector</dt>
<dd>A numeric vector representing the constants in the linear system.</dd>

</dl>

get_cox_qr_solve: Solve Linear System using QR Decomposition

Description

Usage

Value

Arguments