Flexible mixture regression with the generalized hyperbolic distribution

Abstract

When modeling the functional relationship between a response variable and covariates via linear regression, multiple relationships may be present depending on the underlying component structure. Deploying a flexible mixture distribution can help with capturing a wide variety of such structures, thereby successfully modeling the response–covariate relationship while addressing the components. In that spirit, a mixture regression model based on the finite mixture of generalized hyperbolic distributions is introduced, and its parameter estimation method is presented. The flexibility of the generalized hyperbolic distribution can identify better-fitting components, which can lead to a more meaningful functional relationship between the response variable and the covariates. In addition, we introduce an iterative component combining procedure to aid the interpretability of the model. The results from simulated and real data analyses indicate that our method offers a distinctive edge over some of the existing methods, and that it can generate useful insights on the data set at hand for further investigation.