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Mixture network autoregressive model with application on students' successes |
Weizhong TIAN1(), Fengrong WEI2, Thomas BROWN1 |
1. Department of Mathematical Sciences, Eastern New Mexico University, Portales, NM 88130, USA 2. Department of Mathematics, University of West Georgia, Carrollton, GA 30118, USA |
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Abstract We propose a mixture network regression model which considers both response variables and the node-specific random vector depend on the time. In order to estimate and compare the impacts of various connections on a response variable simultaneously, we extend it into p different types of connections. An ordinary least square estimators of the effects of different types of connections on a response variable is derived with its asymptotic property. Simulation studies demonstrate the effectiveness of our proposed method in the estimation of the mixture autoregressive model. In the end, a real data illustration on the students' GPA is discussed.
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Keywords
Network regression
multiple connections
heterogeneous
dynamic effects
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Corresponding Author(s):
Weizhong TIAN
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Issue Date: 09 March 2020
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