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.
Original language | English |
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Article number | https://doi.org/10.1093/jrsssc/qlad041 |
Pages (from-to) | 1064-1078 |
Journal | Journal of the Royal Statistical Society. Series C: Applied Statistics |
Volume | 72 |
Issue number | 4 |
DOIs | |
Publication status | Published - Aug 2023 |
Keywords
- cross-validation
- degrees of freedom
- generalised linear model
- regularisation