pfactor_nllk

log-likelihood Gaussian p-factor structure correlation matrix with gradient

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

pfactor_nllk function

<dl><dt>rhvec</dt>
<dd>vector of length d*p with partial corr representation of loadings</dd><dt>Robs</dt>
<dd>dxd empirical correlation matrix</dd><dt>nsize</dt>
<dd>sample size</dd></dl>

Arguments

log-likelihood Gaussian p-factor structure correlation matrix — pfactor_nllk

<dl>

<dt>rhvec</dt>
<dd>vector of length d*p with partial corr representation of loadings</dd>

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

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

</dl>

pfactor_nllk: log-likelihood Gaussian p-factor structure correlation matrix

Description

Usage

Value

Arguments