Continuous piecewise linear models constitute useful tools to extract the basic features about the patterns of growth in complex time series data. In this work, we present an iterative algorithm for continuous piecewise regression with automatic change-points estimation. The algorithm requires an initial guess about the number and positions of the change-points or hinges, which can be obtained ...

ε-support vector regression (ε-SVR) can be converted into an unconstrained convex and non-smooth quadratic programming problem. It is not solved by the traditional algorithm. In order to solve this non-smooth problem, a class of piecewise smooth functions is introduced to approximate the ε-insensitive loss function of ε-SVR, which generates a ε-piecewise smooth support vector regression (ε-dPWS...

We propose a penalization algorithm for functional linear regression models, where the coefficient function β is shrunk towards data-driven shape template γ. To best of our knowledge, we employ nonzero centered L2 penalty in novel manner, as center γ also optimized while being constrained to belong class piecewise functions Γ, by restricting its basis expansion. This indirect allows user contro...

In recent years, game-theoretic Shapley values have gained increasing attention with respect to local model explanation by feature attributions. While the approach using is model-independent, their (exact) computation usually intractable, so efficient model-specific algorithms been devised including approaches for decision trees or ensembles in general. Our work goes further this direction exte...

Although regression trees were originally designed for large datasets, they can profitably be used on small datasets as well, including those from replicated or unreplicated complete factorial experiments. We show that in the latter situations, regression tree models can provide simpler and more intuitive interpretations of interaction effects as differences between conditional main effects. We...