Pseudo-value regression trees

Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Estimation in survival analysis and censored data (62N02) Survival analysis and censored data (62Nxx)

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
Greedy function approximation: A gradient boosting machine.
Model-based boosting in R: a hands-on tutorial using the R package mboost
Longitudinal data analysis using generalized linear models
Random survival forests
Boosting algorithms: regularization, prediction and model fitting
Pseudo-observations for competing risks with covariate dependent censoring
On pseudo-values for regression analysis in competing risks models
Asymptotic theory of generalized estimating equations based on jack-knife pseudo-observations
Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
Unified methods for censored longitudinal data and causality
Tree-based multivariate regression and density estimation with right-censored data
Survival trees with time-dependent covariates: Application to estimating changes in the incubation period of AIDS
Mixture survival trees for cancer risk classification
Tree-based modeling of time-varying coefficients in discrete time-to-event models
Generalized M‐fluctuation tests for parameter instability
Predicting cumulative incidence probability by direct binomial regression
Generalised linear models for correlated pseudo-observations, with applications to multi-state models
Boosting With theL₂Loss
Most Likely Transformations
A note on pseudo‐observations and left‐truncation
Tree-Based Methods for Statistical Learning in R
Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function
Conditional Transformation Models
Logistic model trees
Subgroups from regression trees with adjustment for prognostic effects and postselection inference
Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: a pseudo-observation approach

This page was built for publication: Pseudo-value regression trees