Semiparametric Bayesian Gaussian copula estimation and imputation

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Description

This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.

Details

Package: sbgcop
Type: Package
Version: 0.975
Date: 2010-03-08
License: GPL Version 2 or later

This function produces MCMC samples from the posterior distribution of a correlation matrix, using a scaled inverse-Wishart prior distribution and an extended rank likelihood. It also provides imputation for missing values in a multivariate dataset.

Author(s)

Peter Hoff <hoff@stat.washington.edu>

References

Hoff (2007) “Extending the rank likelihood for semiparametric copula estimation”

Examples

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fit<-sbgcop.mcmc(swiss)
summary(fit)
plot(fit)