mvtPfact_nllk

negative log-likelihood in the p-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

mvtPfact_nllk function

<dl><dt>rhvec</dt>
<dd>vector of length d*p with partial correlation representation of loadings</dd><dt>tdata</dt>
<dd>nxd data set of t(df)-scores</dd><dt>df</dt>
<dd>degree of freedom parameter &gt;0</dd></dl>

Arguments

negative log-likelihood for the p-factor Gaussian/t model — mvtPfact_nllk

<dl>

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

<dt>tdata</dt>
<dd>nxd data set of t(df)-scores</dd>

<dt>df</dt>
<dd>degree of freedom parameter &gt;0</dd>

</dl>

mvtPfact_nllk: negative log-likelihood for the p-factor Gaussian/t model

Description

Usage

Value

Arguments