Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles

Model-based random forests for ordinal regression

(2020) Muriel Buri et al.   International Journal of Biostatistics

cubremot: a tool for building model-based trees for ordinal responses

(2019) C. Cappelli et al.   EXPERT SYSTEMS WITH APPLICATIONS

Generalized random forests

(2019) Susan Athey et al.   ANNALS OF STATISTICS

The class of cub models: statistical foundations, inferential issues and empirical evidence

(2019) Domenico Piccolo et al.   Statistical Methods and Applications

Ensemble of optimal trees, random forest and random projection ensemble classification

(2019) Zardad Khan et al.   Advances in Data Analysis and Classification

A method for ordinal outcomes: The ordered stereotype model

(2019) Daniel Fernandez et al.   INTERNATIONAL JOURNAL OF METHODS IN PSYCHIATRIC RESEARCH

Random forest for ordinal responses: Prediction and variable selection

(2016) Silke Janitza et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching

(2016) Mariangela Sciandra et al.   QUALITY & QUANTITY

A new parsimonious model for ordinal longitudinal data with application to subjective evaluations of a gastrointestinal disease

(2016) Moreno Ursino et al.   STATISTICAL METHODS IN MEDICAL RESEARCH

Correlation and variable importance in random forests

(2016) Baptiste Gregorutti et al.   STATISTICS AND COMPUTING

Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm

(2015) Christophe Biernacki et al.   STATISTICS AND COMPUTING

rpartOrdinal: AnRPackage for Deriving a Classification Tree for Predicting an Ordinal Response

(2015) Kellie J. Archer   Journal of Statistical Software

Classification Trees for Ordinal Responses inR: TherpartScorePackage

(2015) Giuliano Galimberti et al.   Journal of Statistical Software

Inference on Counterfactual Distributions

(2013)   ECONOMETRICA

A weighted random forests approach to improve predictive performance

(2013) Stacey J. Winham et al.   Statistical Analysis and Data Mining

An Expanded Derivation of the Threshold Structure of the Polytomous Rasch Model That Dispels Any “Threshold Disorder Controversy”

(2012) David Andrich   EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT

A new variable importance measure for random forests with missing data

(2012) Alexander Hapfelmeier et al.   STATISTICS AND COMPUTING

Modeling wine preferences by data mining from physicochemical properties

(2009) Paulo Cortez et al.   DECISION SUPPORT SYSTEMS

Conditional Variable Importance for Random Forests

(2008) Carolin Strobl et al.   BMC BIOINFORMATICS

Graphical diagnostics to check model misspecification for the proportional odds regression model

(2008) Ivy Liu et al.   STATISTICS IN MEDICINE