Mixtures of linear regressions
Computational Statistics & Data Analysis
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Modeling with Mixtures of Linear Regressions
Statistics and Computing
Finite mixture regression model with random effects: application to neonatal hospital length of stay
Computational Statistics & Data Analysis
Adaptive mixtures of local experts
Neural Computation
On convergence rates of mixtures of polynomial experts
Neural Computation
Robust mixture regression using the t-distribution
Computational Statistics & Data Analysis
Mixtures of regressions with changepoints
Statistics and Computing
Hi-index | 0.03 |
We extend the standard mixture of linear regressions model by allowing the mixing proportions to be modeled nonparametrically as a function of the predictors. This framework allows for more flexibility in the modeling of the mixing proportions than the fully parametric mixture of experts model, which we also discuss. We present an EM-like algorithm for estimation of the new model. We also provide simulations demonstrating that our nonparametric approach can provide a better fit than the parametric approach in some instances and can serve to validate and thus reinforce the parametric approach in others. We also analyze and interpret two real data sets using the new method.