Gaining insight with recursive partitioning of generalized linear models

By Thomas Rusch, Achim Zeileis. 2013

35 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Stefan Wager
2. Susan Athey
3. Julie Tibshirani
4. Rina Friedberg
5. Wei-Yin Loh
6. Guido W. Imbens
7. Torsten Hothorn
8. Mihaela van der Schaar

In Computer Networks and Communications

1. Wei-Yin Loh
2. Susan Athey
3. Stefan Wager

In Statistics and Probability

1. Susan Athey
2. Stefan Wager
3. Julie Tibshirani
4. Rina Friedberg
5. Guido W. Imbens
6. Mihaela van der Schaar
7. Wei-Yin Loh
8. Ahmed M. Alaa

Citations in the corpus

• Local Linear Forests (2018, background): “Zeileis, Hothorn, and Hornik (2008), and later Rusch and Zeileis (2013), proposed not only prediction, but recursive partitioning via fitting a separate model in each leaf, similar to the residual splitting strategy of local linear forests.”
• Fifty Years of Classification and Regression Trees (2014, methodology): “MOB has been extended to psychometric models such as the Bradley–Terry model (Strobl et al., 2011) and the Rasch model (Strobl et al., 2010) and to generalized linear models and maximum likelihood models with linear predictors (Rusch & Zeileis, 2013).”