Auto-probit Model for Multiple Regimes of Network Effects
Many researchers believe that consumers’ decisions are not only decided by their personal tastes, but also by the decisions of people who are in their networks. On the other hand, social scientists are more interested in consumers’ dichotomous choice. So an auto-probit model accommodating multiple networks are very useful. However, Current methods to investigate multiple autocorrelated network effects on the same group of actors, embedded in social networks, are primitive. Few solutions have been done for two networks (e.g. Doreian 1989), but not easily on more than three. Even fewer solutions when the dependent variable is binary. We developed two solutions, ExpectationMaximization (E-M) and hierarchical Bayesian, for auto-probit models that accommodate multiple network structures. Both solutions are one of the first in their kinds. The behaviors of the solutions, such as the impact of prior distribution, network structures, and sizes of network effects etc, on parameter estimation will also be studied, by using both real and simulated data.