Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171888
Authors: 
Judge, George
Year of Publication: 
2016
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 4 [Year:] 2016 [Issue:] 3 [Pages:] 1-11
Abstract: 
In this paper, we suggest an approach to recovering behavior-related, preference-choice network information from observational data. We model the process as a self-organized behavior based random exponential network-graph system. To address the unknown nature of the sampling model in recovering behavior related network information, we use the Cressie-Read (CR) family of divergence measures and the corresponding information theoretic entropy basis, for estimation, inference, model evaluation, and prediction. Examples are included to clarify how entropy based information theoretic methods are directly applicable to recovering the behavioral network probabilities in this fundamentally underdetermined ill posed inverse recovery problem.
Subjects: 
random exponential networks
binary and weighed networks
inverse problem
adjacency matrix
Cressie-Read family of divergence measures
conditional moment conditions
self organized behavior systems
JEL: 
C1
C10
C24
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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