---
title: "Selection Criteria for Parameters in grasps"
bibliography: ../inst/REFERENCES.bib
vignette: >
  %\VignetteIndexEntry{crit}
  %\VignetteEngine{quarto::html}
  %\VignetteEncoding{UTF-8}
knitr:
  opts_chunk: 
    collapse: false
    comment: '#>'
    echo: true
    fig.align: "center"
    message: false
    results: 'hide'
    warning: false
---


## Introduction


Precision matrix estimation requires selecting appropriate regularization
parameter $\lambda$ to balance sparsity (number of edges) and model fit
(likelihood), and a mixing parameter $\alpha$ to trade off between element-wise
(individual-level) and block-wise (group-level) penalties.


## Background: Negative Log-Likelihood


In a Gaussian graphical model (GGM), the data matrix $X_{n \times p}$
consists of $n$ independent and identically distributed observations
$X_1, \dots, X_n$ drawn from $N_p(\mu,\Sigma)$.
Let $\Omega = \Sigma^{-1}$ denote the precision matrix, and define the empirical
covariance matrix as
$S = n^{-1} \sum_{i=1}^n (X_i-\bar{X})(X_i-\bar{X})^\top$.
Up to an additive constant, the negative log-likelihood (nll) for $\Omega$
simplified to
$$
\mathrm{nll}(\Omega) = \frac{n}{2}[-\log\det(\Omega) + \mathrm{tr}(S\Omega)].
$$
The edge set $E(\Omega)$ is determined by the non-zero off-diagonal entries:
an edge $(i, j)$ is included if and only if $\omega_{ij} \neq 0$ for $i < j$.
The number of edges is therefore given by $\vert E(\Omega) \vert$.


## Selection Criteria


1. AIC: Akaike information criterion [@akaike1973information]


$$
\hat{\Omega}_{\mathrm{AIC}} = {\arg\min}_{\Omega} \left\{
2\,\mathrm{nll}(\Omega) + 2\,\lvert E(\Omega) \rvert \right\}.
$$


2. BIC: Bayesian information criterion [@schwarz1978estimating]


$$
\hat{\Omega}_{\mathrm{BIC}} = {\arg\min}_{\Omega} \left\{
2\,\mathrm{nll}(\Omega) + \log(n)\,\lvert E(\Omega) \rvert \right\}.
$$


3. EBIC: Extended Bayesian information criterion [@chen2008extended; @foygel2010extended]


$$
\hat{\Omega}_{\mathrm{EBIC}} = {\arg\min}_{\Omega} \left\{
2\,\mathrm{nll}(\Omega) + \log(n)\,\lvert E(\Omega) \rvert +
4\,\xi\,\log(p)\,\lvert E(\Omega) \rvert \right\},
$$


where $\xi \in [0,1]$ is a tuning parameter. Setting $\xi = 0$ reduces EBIC to
the classic BIC.


4. HBIC: High dimensional Bayesian information criterion [@wang2013calibrating; @fan2017high]


$$
\hat{\Omega}_{\mathrm{HBIC}} = {\arg\min}_{\Omega} \left\{
2\,\mathrm{nll}(\Omega) + \log[\log(n)]\,\log(p)\,\lvert E(\Omega) \rvert \right\}.
$$


5. $K$-fold cross validation with negative log-likelihood loss.


@fig-cv illustrates the $K$-fold cross-validation procedure used for tuning
the parameters $\lambda$ and $\alpha$. The notation $\#\lambda$ and $\#\alpha$
denotes the number of candidate values considered for $\lambda$ and $\alpha$,
respectively, forming a grid of $\mathrm{\#}\lambda \times \mathrm{\#}\alpha$
total parameter combinations. For each of the $K$ iterations, negative
log-likelihood loss is evaluated for all parameter combinations, yielding
$K$ performance values per combination. The optimal parameter pair is selected
as the one achieving the lowest average loss across the $K$ iterations.


::: {#fig-cv}
![](../man/figures/cv-diagram.png){width=100%}

$K$-fold cross-validation procedure for tuning ($\lambda$, $\alpha$).
:::


## Reference {-}