latentUpdate1factor

Compute new proxies for 1-factor copula for 1-parameter or 2-parameter linking copulas

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

latentUpdate1factor function

<dl><dt>cpar_est</dt>
<dd>estimated parameters (based on complete likelihood with latent variables known or estimated)</dd><dt>udata</dt>
<dd>nxd matrix of data in (0,1)</dd><dt>nq</dt>
<dd>number of nodes for Gaussian-Legendre quadrature</dd><dt>family</dt>
<dd>vector of code for d linking copula families (choices 1,2,4,5,7,10,14,17,20)</dd></dl>

Arguments

Compute new proxies for 1-factor copula based on the mean of observations — latentUpdate1factor

<dl>

<dt>cpar_est</dt>
<dd>estimated parameters (based on complete likelihood with latent variables known or estimated)</dd>

<dt>udata</dt>
<dd>nxd matrix of data in (0,1)</dd>

<dt>nq</dt>
<dd>number of nodes for Gaussian-Legendre quadrature</dd>

<dt>family</dt>
<dd>vector of code for d linking copula families (choices 1,2,4,5,7,10,14,17,20)</dd>

</dl>

latentUpdate1factor: Compute new proxies for 1-factor copula based on the mean of observations

Description

Usage

Value

Arguments

Examples