get_glm_diag_approx_cov

This function computes the diagonal elements of the approximate covariance matrix
for the coefficients in a generalized linear model (GLM). The covariance is derived
from the second derivative (Hessian) of the log-likelihood function.

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_glm_diag_approx_cov function

<dl><dt>X</dt>
<dd>Matrix. The design matrix (predictors) for the GLM.</dd>
<dt>model</dt>
<dd>A fitted GLM model object. The object should contain the fitted values
and prior weights necessary for computing the Hessian.</dd></dl>

Arguments

Compute Diagonal Approximate Covariance Matrix — get_glm_diag_approx_cov

<dl>

<dt>X</dt>
<dd>Matrix. The design matrix (predictors) for the GLM.</dd>


<dt>model</dt>
<dd>A fitted GLM model object. The object should contain the fitted values
and prior weights necessary for computing the Hessian.</dd>

</dl>

get_glm_diag_approx_cov: Compute Diagonal Approximate Covariance Matrix

Description

Usage

Value

Arguments