Featured Paper: Parties, Models, Mobsters: A New Implementation of Model-Based Recursive Partitioning in R

This paper by Zeileis and Hothorn illustrates the ongoing research trend of marrying machine learning with statistics, to combine their strengths: individualization of estimates and proper capture of weak signals on one side, and management & operational guarantees future unknown data on the other side. This particular paper focuses on model-based recursive partitioning.

Abstract: MOB is a generic algorithm for model-based recursive partitioning (Zeileis, Hothorn,
and Hornik 2008). Rather than fitting one global model to a dataset, it estimates local
models on subsets of data that are “learned” by recursively partitioning. It proceeds in the
following way: (1) fit a parametric model to a data set, (2) test for parameter instability
over a set of partitioning variables, (3) if there is some overall parameter instability, split
the model with respect to the variable associated with the highest instability, (4) repeat
the procedure in each of the resulting subsamples. It is discussed how these steps of the
conceptual algorithm are translated into computational tools in an object-oriented manner,
allowing the user to plug in various types of parametric models. For representing the
resulting trees, the R package partykit is employed and extended with generic infrastructure
for recursive partitions where nodes are associated with statistical models. Compared
to the previously available implementation in the party package, the new implementation
supports more inference options, is easier to extend to new models, and provides more
convenience features.

Full paper

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