Varying coefficient models have been widely used in longitudinal data analysis, nonlinear time series, survival analysis, and so on. They are natural non-parametric extensions of the classical linear models in many contexts, keeping good interpretability and allowing us to explore the dynamic nature of the model. Recently, penalized estimators have been used for fitting varying-coefficient mode...

the large number of applications manages time varying data. existing databasetechnology seldom supports temporal database, tdb, according to time aspects.these intrinsic temporal database applications rely on such database which storesand retrieves time referenced data. moreover, applications need to be managed oncommon data items access simultaneously and to be precluded from inconsistencyas s...

Abstract Varying coefficient models are a flexible extension of generic parametric whose coefficients functions set effect-modifying covariates instead fitted constants. They capable achieving higher model complexity while preserving the structure underlying models, hence generating interpretable predictions. In this paper we study use gradient boosted decision trees as those coefficient-decidi...