Two-dimensional fused targeted ridge regression for health indicator prediction from accelerometer data

Abstract

We present the two-dimensional targeted fused ridge estimator of the linear and logistic regression models. The estimator (i) handles both unpenalised and penalised covariates, (ii) accommodates possible relations among the covariates’ coefficients through a fusion penalty, and (iii) incorporates prior information on the regression parameter through a non-zero shrinkage target. In this work, the aforementioned relations are similarities among the covariates’ coefficients due to spatial proximity in a two-dimensional grid. In an extensive re-analysis of an epidemiological and an image analysis study, we illustrate the use of the estimator’s aforementioned features that result in a tangibly interpretable predictor.