Alexander Hanbo Li, Jelena Bradic
arXiv 8 Feb 2019 · Statistics — Machine Learning · 8 citations (OpenAlex)
arXiv:1902.03327 · PDF · DOI · OpenAlex · Extracted main text
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on new estimating equations that adapt to censoring and lead to quantile score whenever the data do not exhibit censoring. The proposed procedure named censored quantile regression forest, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.
appendix boundary found by appendix_titled_section at “Appendix” · 93% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athey, S., J. Tibshirani, and S. Wager (2018) Generalized random forests | 1.000 | 7 | 4 | 100% |
| 2 | Meinshausen, N (2006) Quantile regression forests | 0.961 | 9 | 5 | 89% |
| 3 | Breiman, L (2001) Random forests | 0.737 | 3 | 2 | 100% |
| 4 | Hothorn, T., P. Bühlmann, S. Dudoit, A. Molinaro, and M. J. Van Der… (2005) Survival ensembles | 0.737 | 3 | 2 | 100% |
| 5 | Dabrowska, D. M (1989) Uniform consistency of the kernel conditional kaplan-meier estimate | 0.644 | 2 | 2 | 100% |
| 6 | Ishwaran, H., U. B. Kogalur, E. H. Blackstone, and M. S. Lauer (2008) Random survival forests | 0.644 | 2 | 2 | 100% |
| 7 | Li, A. H. and A. Martin (2017) Forest-type regression with general losses and robust forest self | 0.644 | 2 | 2 | 100% |
| 8 | Robins, J. and A. A. Tsiatis (1992) Semiparametric estimation of an accelerated failure time model with time-dependent covariates | 0.644 | 2 | 2 | 100% |
| 9 | Zhu, R. and M. R. Kosorok (2012) Recursively imputed survival trees | 0.644 | 2 | 2 | 100% |
| 10 | Bloniarz, A., A. S. Talwalkar, B. Yu, and C. Wu (2016) Supervised neighborhoods for distributed nonparametric regression | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 42 scored citations.