mvtPfact

MLE in a MVt model with a p-factor correlation structure

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 function

<dl><dt>tdata</dt>
<dd>nxd matrix of t-scores</dd><dt>start</dt>
<dd>vector of length p*d with starting values for partial correlation representation of loadings (algberaically independent)</dd><dt>pfact</dt>
<dd>number of factors (such as 1,2,3,... )</dd><dt>df</dt>
<dd>degree of freedom parameter &gt;0</dd><dt>prlevel</dt>
<dd>print.level for nlm()</dd><dt>mxiter</dt>
<dd>maximum number of iterations for nlm()</dd></dl>

Arguments

MLE in a MVt model with a p-factor correlation structure — mvtPfact

<dl>

<dt>tdata</dt>
<dd>nxd matrix of t-scores</dd>

<dt>start</dt>
<dd>vector of length p*d with starting values for partial correlation representation of loadings (algberaically independent)</dd>

<dt>pfact</dt>
<dd>number of factors (such as 1,2,3,... )</dd>

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

<dt>prlevel</dt>
<dd>print.level for nlm()</dd>

<dt>mxiter</dt>
<dd>maximum number of iterations for nlm()</dd>

</dl>

mvtPfact: MLE in a MVt model with a p-factor correlation structure

Description

Usage

Value

Arguments

Examples