Multithreshold change plane model: Estimation theory and applications in subgroup identification.

ABSTRACT: We propose a multithreshold change plane regression model which naturally partitions the observed subjects into subgroups with different covariate effects. The underlying grouping variable is a linear function of observed covariates and thus multiple thresholds produce change planes in the covariate space. We contribute a novel two-stage estimation approach to determine the number of subgroups, the location of thresholds, and all other regression parameters. In the first stage we adopt a group selection principle to consistently identify the number of subgroups, while in the second stage change point locations and model parameter estimates are refined by a penalized induced smoothing technique. Our procedure allows sparse solutions for relatively moderate- or high-dimensional covariates. W

SUBMITTER: Li J 

PROVIDER: S-EPMC8180507 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature