Mixture network autoregressive model with application on students’successes

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.

Mixture network autoregressive model with application on students' successes

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.