7 citations · 9 across the 13 of their papers we have counts for
4 papers · 1 filter
Consistency of Random Forest Type Algorithms under a Probabilistic Impurity Decrease Condition
Ricardo Blum, Munir Hiabu, Enno Mammen +1
This paper derives a unifying theorem establishing consistency results for a broad class of tree-based algorithms. It improves current results in two aspects. First of all, it can…
Smooth Backfitting for Additive Hazard Rates
Stephan M. Bischofberger, Munir Hiabu, Enno Mammen +1
Smooth backfitting was first introduced in an additive regression setting via a direct projection alternative to the classic backfitting method by Buja, Hastie and Tibshirani. This…
Local linear smoothing in additive models as data projection
Munir Hiabu, Enno Mammen, Joseph T. Meyer
We discuss local linear smooth backfitting for additive non-parametric models. This procedure is well known for achieving optimal convergence rates under appropriate smoothness con…
Smooth backfitting of proportional hazards with multiplicative components
Munir Hiabu, Enno Mammen, Maria Dolores Martinez-Miranda +1
Smooth backfitting has proven to have a number of theoretical and practical advantages in structured regression. Smooth backfitting projects the data down onto the structured space…