Title
Maximum Entropy Expectation-Maximization Algorithm for Fitting Latent-Variable Graphical Models to Multivariate Time Series.
Abstract
This work is focused on latent-variable graphical models for multivariate time series. We show how an algorithm which was originally used for finding zeros in the inverse of the covariance matrix can be generalized such that to identify the sparsity pattern of the inverse of spectral density matrix. When applied to a given time series, the algorithm produces a set of candidate models. Various information theoretic (IT) criteria are employed for deciding the winner. A novel IT criterion, which is tailored to our model selection problem, is introduced. Some options for reducing the computational burden are proposed and tested via numerical examples. We conduct an empirical study in which the algorithm is compared with the state-of-the-art. The results are good, and the major advantage is that the subjective choices made by the user are less important than in the case of other methods.
Year
DOI
Venue
2018
10.3390/e20010076
ENTROPY
Keywords
Field
DocType
maximum entropy,Expectation-Maximization,graphical models,autoregressive model,latent variables,information theoretic criteria,time series
Autoregressive model,Mathematical optimization,Expectation–maximization algorithm,Matrix (mathematics),Model selection,Latent variable,Principle of maximum entropy,Graphical model,Covariance matrix,Mathematics
Journal
Volume
Issue
ISSN
20
1
1099-4300
Citations 
PageRank 
References 
1
0.36
9
Authors
3
Name
Order
Citations
PageRank
Said Maanan110.70
Bogdan Dumitrescu210722.76
Ciprian Doru Giurcaneanu34312.44