Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models
| Autor: | Zack W. Almquist, Abhirup Mallik |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Dynamic network analysis Computer science Computation Logit Inference Logistic regression computer.software_genre Statistics - Computation 01 natural sciences Network simulation Methodology (stat.ME) 010104 statistics & probability 0502 economics and business Discrete Mathematics and Combinatorics 0101 mathematics Statistics - Methodology Computation (stat.CO) 050205 econometrics 05 social sciences Statistical model Regression analysis Data mining Statistics Probability and Uncertainty computer |
| Zdroj: | Journal of Computational and Graphical Statistics. 28:967-979 |
| ISSN: | 1537-2715 1061-8600 |
| DOI: | 10.1080/10618600.2019.1594834 |
| Popis: | Recent developments in computers and automated data collection strategies have greatly increased the interest in statistical modeling of dynamic networks. Many of the statistical models employed for inference on large-scale dynamic networks suffer from limited forward simulation/prediction ability. A major problem with many of the forward simulation procedures is the tendency for the model to become degenerate in only a few time steps, i.e., the simulation/prediction procedure results in either null graphs or complete graphs. Here, we describe an algorithm for simulating a sequence of networks generated from lagged dynamic network regression models DNR(V), a sub-family of TERGMs. We introduce a smoothed estimator for forward prediction based on smoothing of the change statistics obtained for a dynamic network regression model. We focus on the implementation of the algorithm, providing a series of motivating examples with comparisons to dynamic network models from the literature. We find that our algorithm significantly improves multi-step prediction/simulation over standard DNR(V) forecasting. Furthermore, we show that our method performs comparably to existing more complex dynamic network analysis frameworks (SAOM and STERGMs) for small networks over short time periods, and significantly outperforms these approaches over long time time intervals and/or large networks. |
| Databáze: | OpenAIRE |
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