Econometric Information Recovery in Behavioral Networks
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 beha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Econometrics |
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| Online Access: | http://www.mdpi.com/2225-1146/4/3/38 |
| _version_ | 1798002499298263040 |
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| author | George Judge |
| author_facet | George Judge |
| author_sort | George Judge |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T11:53:07Z |
| format | Article |
| id | doaj.art-7b3fe1fdb8fa4a17ad583bb4be135715 |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-04-11T11:53:07Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-7b3fe1fdb8fa4a17ad583bb4be1357152022-12-22T04:25:15ZengMDPI AGEconometrics2225-11462016-09-01433810.3390/econometrics4030038econometrics4030038Econometric Information Recovery in Behavioral NetworksGeorge Judge0Graduate School and Giannini Foundation, 207 Giannini Hall, University of California Berkeley, Berkeley, CA 94720, USAIn 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.http://www.mdpi.com/2225-1146/4/3/38random exponential networksbinary and weighed networksinverse problemadjacency matrixCressie-Read family of divergence measuresconditional moment conditionsself organized behavior systems |
| spellingShingle | George Judge Econometric Information Recovery in Behavioral Networks Econometrics random exponential networks binary and weighed networks inverse problem adjacency matrix Cressie-Read family of divergence measures conditional moment conditions self organized behavior systems |
| title | Econometric Information Recovery in Behavioral Networks |
| title_full | Econometric Information Recovery in Behavioral Networks |
| title_fullStr | Econometric Information Recovery in Behavioral Networks |
| title_full_unstemmed | Econometric Information Recovery in Behavioral Networks |
| title_short | Econometric Information Recovery in Behavioral Networks |
| title_sort | econometric information recovery in behavioral networks |
| topic | random exponential networks binary and weighed networks inverse problem adjacency matrix Cressie-Read family of divergence measures conditional moment conditions self organized behavior systems |
| url | http://www.mdpi.com/2225-1146/4/3/38 |
| work_keys_str_mv | AT georgejudge econometricinformationrecoveryinbehavioralnetworks |