Bartolucci, Francesco and Marino, Maria Francesca and Pandolfi, Silvia (2015): Composite likelihood inference for hidden Markov models for dynamic networks.

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Abstract
We introduce a hidden Markov model for dynamic network data where directed relations among a set of units are observed at different time occasions. The model can also be used with minor adjustments to deal with undirected networks. In the directional case, dyads referred to each pair of units are explicitly modelled conditional on the latent states of both units. Given the complexity of the model, we propose a composite likelihood method for making inference on its parameters. This method is studied in detail for the directional case by a simulation study in which different scenarios are considered. The proposed approach is illustrated by an example based on the wellknown Enron dataset about email exchange.
Item Type:  MPRA Paper 

Original Title:  Composite likelihood inference for hidden Markov models for dynamic networks 
Language:  English 
Keywords:  Dyads; EM algorithm; Enron dataset; Latent Markov models 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C18  Methodological Issues: General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables 
Item ID:  67242 
Depositing User:  Dr. Maria Francesca Marino 
Date Deposited:  16 Oct 2015 06:38 
Last Modified:  26 Sep 2019 08:58 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/67242 