论文标题
平滑的半参数可能性，用于估计具有基于Copula的依赖性结构的非参数有限混合模型
A smoothed semiparametric likelihood for estimation of nonparametric finite mixture models with a copula-based dependence structure
论文作者
论文摘要
在此手稿中，我们考虑了有限的多元非参数混合模型，其中使用Copula设备对边缘密度之间的依赖性进行了建模。最近提出了伪EM随机算法，以在边际限制下估算该模型的所有组成部分。在这里，我们介绍了一种确定性算法，该算法旨在最大化平滑的半参数可能性。没有对边际的位置规模的假设。在一种特殊情况下，该算法是单调的，在另一种情况下，该算法会导致``近似单调性''，因此，目标函数的连续值之间的差异变得无负术语，直到在足够多的迭代后变得可忽略不计。该算法的行为在几个模拟数据集上说明。结果表明，在适当的条件下，所提出的算法通常确实是单调的。讨论结果以及一些可能的未来研究指示，我们的演讲结束了。
In this manuscript, we consider a finite multivariate nonparametric mixture model where the dependence between the marginal densities is modeled using the copula device. Pseudo EM stochastic algorithms were recently proposed to estimate all of the components of this model under a location-scale constraint on the marginals. Here, we introduce a deterministic algorithm that seeks to maximize a smoothed semiparametric likelihood. No location-scale assumption is made about the marginals. The algorithm is monotonic in one special case, and, in another, leads to ``approximate monotonicity'' -- whereby the difference between successive values of the objective function becomes non-negative up to an additive term that becomes negligible after a sufficiently large number of iterations. The behavior of this algorithm is illustrated on several simulated datasets. The results suggest that, under suitable conditions, the proposed algorithm may indeed be monotonic in general. A discussion of the results and some possible future research directions round out our presentation.
