Estimation of a common mean vector in bivariate meta-analysis under the FGM copula
Estimation of a common mean vector in bivariate meta-analysis under the FGM copula
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FGM copula 下双变量荟萃分析中共同均值向量的估计
DOI：
10.1080/02331888.2019.1581782
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发表时间：
2019
期刊：
影响因子：
1.9
通讯作者：
Emura Takeshi
中科院分区：
文献类型：
--
作者：
Shih Jia-Han;Konno Yoshihiko;Chang Yuan-Tsung;Emura Takeshi
我们提出了用于双变量荟萃分析的双变量 Farlie-Gumbel-Morgenstern (FGM) 联结模型，并开发了共同均值向量的最大似然估计器。借助 FGM 联结函数的新颖数学恒等式，我们推导了 Fisher 信息矩阵的表达式。我们还推导了Fisher信息矩阵的近似公式，该公式准确且易于计算。基于独立但不同分布（i.n.i.d.）样本的理论，我们检查了估计量的渐近性质。仿真研究证明了所提出方法的性能，并提供了真实数据分析来说明该方法。
We propose a bivariate Farlie–Gumbel–Morgenstern (FGM) copula model for bivariate meta-analysis, and develop a maximum likelihood estimator for the common mean vector. With the aid of novel mathematical identities for the FGM copula, we derive the expression of the Fisher information matrix. We also derive an approximation formula for the Fisher information matrix, which is accurate and easy to compute. Based on the theory of independent but not identically distributed (i.n.i.d.) samples, we examine the asymptotic properties of the estimator. Simulation studies are given to demonstrate the performance of the proposed method, and a real data analysis is provided to illustrate the method.