Global Censored Quantile Random Forest

TL;DR

The paper introduces GCQRF, a flexible forest-based method for censored quantile regression that captures complex nonlinear relationships and provides feature importance measures, demonstrating superior predictive accuracy.

Contribution

It proposes a novel global censored quantile random forest method that handles right censoring and nonlinearities, with theoretical convergence analysis and feature importance measures.

Findings

Outperforms existing methods in predictive accuracy

Provides feature importance rankings based on out-of-sample accuracy

Demonstrates effectiveness on simulated and real datasets

Abstract

In recent years, censored quantile regression has enjoyed an increasing popularity for survival analysis while many existing works rely on linearity assumptions. In this work, we propose a Global Censored Quantile Random Forest (GCQRF) for predicting a conditional quantile process on data subject to right censoring, a forest-based flexible, competitive method able to capture complex nonlinear relationships. Taking into account the randomness in trees and connecting the proposed method to a randomized incomplete infinite degree U-process (IDUP), we quantify the prediction process' variation without assuming an infinite forest and establish its weak convergence. Moreover, feature importance ranking measures based on out-of-sample predictive accuracy are proposed. We demonstrate the superior predictive accuracy of the proposed method over a number of existing alternatives and illustrate the use of the proposed importance ranking measures on both simulated and real data.