Markov models for digraph panel data: Monte Carlo-based derivative estimation
Markov models for digraph panel data: Monte Carlo-based derivative estimation
复制标题
DOI：
10.1016/j.csda.2006.07.014
复制
发表时间：
2007-05-15
影响因子：
1.8
通讯作者：
Snijders, Tom A. B.
中科院分区：
文献类型：
--
作者：
Schweinberger, Michael;Snijders, Tom A. B.
考虑了有向图面板数据的参数连续时间马尔可夫模型。用矩量法估计参数。一种用于估计矩估计量的方差-协方差矩阵的方便方法依赖于delta方法，需要估计函数的雅可比矩阵，即偏导数矩阵。雅可比矩阵估计迄今为止的Monte Carlo方法的基础上有限差分。提出了三种新的Jacobian矩阵的Monte Carlo估计，它们与导数估计的似然比/得分函数法有关，与有限差分法相比具有理论和实际上的优点。一些光的实际性能的方法，通过将它们应用在真正的雅可比矩阵是已知的情况下，在真正的雅可比矩阵是未知的情况下。(c)2006 Elsevier B. V.保留所有权利。
A parametric, continuous-time Markov model for digraph panel data is considered. The parameter is estimated by the method of moments. A convenient method for estimating the variance-covariance matrix of the moment estimator relies on the delta method, requiring the Jacobian matrix-that is, the matrix of partial derivatives-of the estimating function. The Jacobian matrix was estimated hitherto by Monte Carlo methods based on finite differences. Three new Monte Carlo estimators of the Jacobian matrix are proposed, which are related to the likelihood ratio/score function method of derivative estimation and have theoretical and practical advantages compared to the finite differences method. Some light is shed on the practical performance of the methods by applying them in a situation where the true Jacobian matrix is known and in a situation where the true Jacobian matrix is unknown. (c) 2006 Elsevier B.V. All rights reserved.