Prediction with a flexible finite mixture-of-regressions

Computational Statistics & Data Analysis(2019)

引用 3|浏览37
暂无评分
摘要
Finite mixture regression (FMR) is widely used for modeling data that originate from heterogeneous populations. In these settings, FMR can offer increased predictive power compared to more traditional one-class models. However, existing FMR methods rely heavily on mixtures of linear models, where the linear predictor must be given as an input. A flexible FMR model is presented using a combination of the random forest learner and a penalized linear FMR. The performance of the new method is assessed by predictive log-likelihood in extensive simulation studies. The method is shown to achieve equal performance with the existing FMR methods when the true regression functions are in fact linear and superior performance in cases where at least one of the regression functions is nonlinear. The method can handle a large number of covariates, and its predictive ability is not greatly affected by surplus variables.
更多
查看译文
关键词
Finite mixture regression,Random forest,Prediction intervals,Bootstrap,Penalization
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要