Deep quantile forests for high-dimensional data

Quantile regression is essential for analyzing the relationship between conditional quantiles of independent and dependent variables, widely applied in economics, education, social science, and beyond. However, traditional methods often struggle with nonlinearity, high dimensionality, and other complexities in data. To address these challenges, this paper innovatively integrates the cascade architecture into quantile regression forests, proposing the deep quantile forest method. Unlike existing deep-quantile regression estimators, our approach requires fewer hyperparameters and less training data while offering better performance and enhanced interpretability. Extensive numerical simulations and real-world data experiments demonstrate that our proposed method outperforms competing methods, showcasing its effectiveness and robustness in handling the complex structures of high-dimensional data.