Semiparametric Bayesian Gaussian copula estimation and imputation
半参数贝叶斯的高斯Copula函数估计和估算
译者:生物统计家园网 机器人LoveR
描述----------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.
这个包估计的高斯Copula函数的参数,处理单变量的边缘分布霍夫(2007年)中所描述的滋扰参数。它还提供了一个半参数归集为多元数据丢失的程序。
Details
详细信息----------Details----------
</table> 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.
</ TABLE>此功能MCMC样本的后验分布的相关性矩阵,使用规模较小的逆Wishart先验分布和扩展排名的可能性。它还提供了多变量数据集缺失值插补。
(作者)----------Author(s)----------
Peter Hoff <hoff@stat.washington.edu>
参考文献----------References----------
Hoff (2007) “Extending the rank likelihood for semiparametric copula estimation”