mvtBifact_nllk

negative log-likelihood in the bi-factor Gaussian or t model

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

mvtBifact_nllk function

<dl><dt>rhovec</dt>
<dd>vector of length d*2 for partial correlation representation of loadings, first d correlations with common factor, then partial correlations with group factor given common factor</dd><dt>grsize</dt>
<dd>vector of group sizes for bi-factor model</dd><dt>tdata</dt>
<dd>nxd data set of scores for t df (e.g., qt(u,df))</dd><dt>df</dt>
<dd>degree of freedom parameter (positive)</dd></dl>

Arguments

negative log-likelihood for the bi-factor Gaussian/t model — mvtBifact_nllk

<dl>

<dt>rhovec</dt>
<dd>vector of length d*2 for partial correlation representation of loadings, first d correlations with common factor, then partial correlations with group factor given common factor</dd>

<dt>grsize</dt>
<dd>vector of group sizes for bi-factor model</dd>

<dt>tdata</dt>
<dd>nxd data set of scores for t df (e.g., qt(u,df))</dd>

<dt>df</dt>
<dd>degree of freedom parameter (positive)</dd>

</dl>

mvtBifact_nllk: negative log-likelihood for the bi-factor Gaussian/t model

Description

Usage

Value

Arguments