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Econometrics 2016, 4(3), 38; doi:10.3390/econometrics4030038

Econometric Information Recovery in Behavioral Networks

Graduate School and Giannini Foundation, 207 Giannini Hall, University of California Berkeley, Berkeley, CA 94720, USA
Academic Editor: Kerry Patterson
Received: 30 November 2015 / Revised: 30 August 2016 / Accepted: 30 August 2016 / Published: 14 September 2016
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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. View Full-Text
Keywords: random exponential networks; binary and weighed networks; inverse problem; adjacency matrix; Cressie-Read family of divergence measures; conditional moment conditions; self organized behavior systems random exponential networks; binary and weighed networks; inverse problem; adjacency matrix; Cressie-Read family of divergence measures; conditional moment conditions; self organized behavior systems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Judge, G. Econometric Information Recovery in Behavioral Networks. Econometrics 2016, 4, 38.

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