Calibration estimation of semiparametric copula models with data missing at random
Calibration estimation of semiparametric copula models with data missing at random
复制标题
DOI：
10.1016/j.jmva.2019.02.003
复制
发表时间：
2019-09-01
影响因子：
1.6
通讯作者：
Zhang, Zheng
中科院分区：
文献类型：
--
作者：
Hamori, Shigeyuki;Motegi, Kaiji;Zhang, Zheng
本文研究了数据随机缺失的半参数联结模型的估计。 Genest 等人的最大伪似然估计。如果存在缺失数据，(1995) 是不可行的。我们通过平衡观察组和整个组之间协变量的经验矩，提出了一类非参数边缘分布和感兴趣的联结参数的校准估计量。我们提出的估计器不需要估计缺失的机制，即使样本量很小，它们也具有稳定的性能。我们证明我们的估计量满足一致性和渐近正态性。我们还为渐近方差提供了一致的估计量。我们通过广泛的模拟表明，我们提出的方法主导了现有的替代方法。 (C) 2019 Elsevier Inc. 保留所有权利。
This paper investigates the estimation of semiparametric copula models with data missing at random. The maximum pseudo-likelihood estimation of Genest et al. (1995) is infeasible if there are missing data. We propose a class of calibration estimators for the nonparametric marginal distributions and the copula parameters of interest by balancing the empirical moments of covariates between observed and whole groups. Our proposed estimators do not require the estimation of the missing mechanism, and they enjoy stable performance even when the sample size is small. We prove that our estimators satisfy consistency and asymptotic normality. We also provide a consistent estimator for the asymptotic variance. We show via extensive simulations that our proposed method dominates existing alternatives. (C) 2019 Elsevier Inc. All rights reserved.