论文标题
部分可观测时空混沌系统的无模型预测
Efficient and feasible inference for high-dimensional normal copula regression models
论文作者
论文摘要
复合可能性（CL）是用于估计具有离散响应的高维多元正常（MVN）Copula模型的计算方法之一。它作为替代可能性方法的计算优势是基于单变量回归和非回归参数的独立性可能性，以及相关参数的成对可能性，但是估计单变量回归和非回归参数的效率可能很低。对于高维离散响应，我们提出了复合可能性估计方程的加权版本和确定良好重量矩阵的迭代方法。将一般方法应用于以单变量回归为边缘的MVN副群。效率计算表明，我们的方法几乎与完全指定的MVN Copula模型的最大可能性一样有效。插图包括与协变量有关纵向（低维）和时间（高维）序列响应数据的模拟和真实数据应用，并显示出通过加权CL方法的效率有了可观的提高。
The composite likelihood (CL) is amongst the computational methods used for the estimation of high-dimensional multivariate normal (MVN) copula models with discrete responses. Its computational advantage, as a surrogate likelihood method, is that is based on the independence likelihood for the univariate regression and non-regression parameters and pairwise likelihood for the correlation parameters, but the efficiency of estimating the univariate regression and non-regression parameters can be low. For a high-dimensional discrete response, we propose weighted versions of the composite likelihood estimating equations and an iterative approach to determine good weight matrices. The general methodology is applied to the MVN copula with univariate ordinal regressions as the marginals. Efficiency calculations show that our method is nearly as efficient as the maximum likelihood for fully specified MVN copula models. Illustrations include simulations and real data applications regarding longitudinal (low-dimensional) and time (high-dimensional) series ordinal response data with covariates and it is shown that there is a substantial gain in efficiency via the weighted CL method.
