oblique_grad_nllk

log-likelihood Gaussian oblique factor structure correlation matrix with gradient
for d variables and m groups,

Inference methods for factor copula models for continuous data in Krupskii and Joe (2013) <doi:10.1016/j.jmva.2013.05.001>, Krupskii and Joe (2015) <doi:10.1016/j.jmva.2014.11.002>, Fan and Joe (2024) <doi:10.1016/j.jmva.2023.105263>, one factor truncated vine models in Joe (2018) <doi:10.1002/cjs.11481>, and Gaussian oblique factor models. Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) <doi:10.1080/10485252.2017.1407414> and estimating tail dependence parameter.

Pavel Krupskii

FactorCopulaModel

Factor Copula Models

Harry Joe

Xinyao Fan

Allan Macleod

Robert Gentleman

Ross Ihaka

oblique_grad_nllk function

<dl><dt>theta</dt>
<dd>vector of length d + m*(m-1)/2; d loading parameters followed by m*(m-1)/2 entries in correlation matrix of latent variables (lower triangle by row)</dd><dt>grsize</dt>
<dd>vector of group sizes (variables ordered by group)</dd><dt>Robs</dt>
<dd>dxd empirical correlation matrix</dd><dt>nsize</dt>
<dd>sample size if available</dd></dl>

Arguments

log-likelihood Gaussian oblique factor structure correlation matrix — oblique_grad_nllk

<dl>

<dt>theta</dt>
<dd>vector of length d + m*(m-1)/2; d loading parameters followed by m*(m-1)/2 entries in correlation matrix of latent variables (lower triangle by row)</dd>

<dt>grsize</dt>
<dd>vector of group sizes (variables ordered by group)</dd>

<dt>Robs</dt>
<dd>dxd empirical correlation matrix</dd>

<dt>nsize</dt>
<dd>sample size if available</dd>

</dl>

oblique_grad_nllk: log-likelihood Gaussian oblique factor structure correlation matrix

Description

Usage

Value

Arguments