Unifying framework for CTree, MOB and GUIDE shows model scores without dichotomization yield higher power for covariate selection than residuals or dichotomized scores in many scenarios.
hub
Unbiased Recursive Partitioning: A Conditional Inference Framework
4 Pith papers cite this work, alongside 4,058 external citations. Polarity classification is still indexing.
4
Pith papers citing it
4,058
external citations · OpenAlex
hub tools
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
The paper introduces a question-driven framework and set of statistical methods for exploratory assessment of regional treatment effect heterogeneity in multi-regional clinical trials, evaluated via simulations under no-heterogeneity and modifier-driven scenarios.
Conditional inference forests rank competitively as top-k feature selectors in classification and regression benchmarks, with runtime factors identified but limited impact on scores.
citing papers explorer
-
The Power of Unbiased Recursive Partitioning: A Unifying View of CTree, MOB, and GUIDE
Unifying framework for CTree, MOB and GUIDE shows model scores without dichotomization yield higher power for covariate selection than residuals or dichotomized scores in many scenarios.
-
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
-
A Workflow for Evaluating Regional Treatment Effect Heterogeneity in Multi-Regional Clinical Trials
The paper introduces a question-driven framework and set of statistical methods for exploratory assessment of regional treatment effect heterogeneity in multi-regional clinical trials, evaluated via simulations under no-heterogeneity and modifier-driven scenarios.
-
Conditional Inference Trees and Forests for Feature Selection
Conditional inference forests rank competitively as top-k feature selectors in classification and regression benchmarks, with runtime factors identified but limited impact on scores.