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Markov models for digraph panel data
- Source :
- Computational Statistics and Data Analysis, 51(9), 4465-4483. ELSEVIER SCIENCE BV
- Publication Year :
- 2007
-
Abstract
- 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.
- Subjects :
- Statistics and Probability
Monte Carlo method
variance reduction
gradient estimation
Control variates
Markov model
control variates
Matrix (mathematics)
symbols.namesake
continuous-time Markov process
Calculus
likelihood ratio/score function method
Applied mathematics
SENSITIVITY ANALYSIS
Mathematics
Applied Mathematics
COMPUTER-SIMULATION MODELS
Estimator
Computational Mathematics
Computational Theory and Mathematics
Jacobian matrix and determinant
symbols
SOCIAL NETWORKS
Likelihood function
Matrix method
digraphs
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Computational Statistics and Data Analysis, 51(9), 4465-4483. ELSEVIER SCIENCE BV
- Accession number :
- edsair.doi.dedup.....76fce339feee9ed6971f457a3df17232