论文标题
高维因子模型模型，并估计潜在变量
High-dimensional factor copula models with estimation of latent variables
论文作者
论文摘要
因子模型是使用几个潜在变量来解释变量依赖性的一种简约方法。在高斯1因子和结构因子模型（例如双因子，倾斜因子）及其因子copula对应物中，因子得分或代理被定义为鉴于观察到的变量的潜在变量的条件期望。使用温和的假设，代理对于相应的潜在变量是一致的，因为样本量和与每个潜在变量相关的观察到的变量的数量转到无穷大。当不提前假设将观察到的变量与潜在变量联系起来的双变量Copulas时，将使用顺序程序进行潜在变量估计，copula家族选择和参数估计。将代理变量用于因子copulas，这意味着近似对数可能性可用于估计副本参数，而计算量较少以进行数值集成。
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.
