Factor models are a parsimonious way to explain the dependence of variables using several latent variables. In Gaussian 1-factor and structural factor models (such as bi-factor, oblique factor) and their factor copula counterparts, factor scores or proxies are defined as conditional expectations of latent variables given the observed variables. With mild assumptions, the proxies are consistent for corresponding latent variables as the sample size and the number of observed variables linked to each latent variable go to infinity. When the bivariate copulas linking observed variables to latent variables are not assumed in advance, sequential procedures are used for latent variables estimation, copula family selection and parameter estimation. The use of proxy variables for factor copulas means that approximate log-likelihoods can be used to estimate copula parameters with less computational effort for numerical integration.

翻译：系数模型是一种用几种潜在变量解释变量依赖性的方法。 在高西亚1-因素和结构系数模型(如双因子、斜因子)及其系数千叶对等物中,系数分数或代数被定义为根据观察到的变量对潜在变量的有条件预期值。用较轻的假设,相对于相应的潜在变量而言,系数分数或代数是一致的,因为样本大小以及与每个潜在变量相联系的观测变量的数量会变成无限。当将观察到的变量与潜在变量相联系的双变量不预先假定时,对潜在变量估计、合极家庭选择和参数估计采用顺序程序。使用系数相近的正叶变数意味着可以使用近于正叶类的近似值来估计相交点参数,而计算工作较少于数字整合的计算努力。