More Like this

1. Envelope Quantile Regression

   https://doi.org/10.5705/ss.202018.0060  

   Ding, Shanshan; Su, Zhihua; Zhu, Guangyu; Wang, Lan (January 2020, Statistica Sinica)

   null (Ed.)
2. Bayesian joint-quantile regression

   https://doi.org/10.1007/s00180-020-00998-w  

   Hu, Yingying; Wang, Huixia Judy; He, Xuming; Guo, Jianhua (January 2020, Computational Statistics)
3. Censored Quantile Regression Forest

   Li, Alexander Hanbo; Bradic, Jelena (January 2020, Proceedings of Machine Learning Research)

   Chiappa, Silvia; Calandra, Roberto (Ed.)

   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 a new estimating equation that adapts to censoring and leads 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. 

   more » « less
4. Quantile-Optimal Treatment Regimes

   https://doi.org/10.1080/01621459.2017.1330204  

   Wang, Lan; Zhou, Yu; Song, Rui; Sherwood, Ben (March 2017, Journal of the American Statistical Association)
5. Composite quantile‐based classifiers

   https://doi.org/10.1002/sam.11460  

   Pritchard, David A.; Liu, Yufeng (May 2020, Statistical Analysis and Data Mining: The ASA Data Science Journal)

   Abstract Accurate classification of high‐dimensional data is important in many scientific applications. We propose a family of high‐dimensional classification methods based upon a comparison of the component‐wise distances of the feature vector of a sample to the within‐class population quantiles. These methods are motivated by the fact that quantile classifiers based on these component‐wise distances are the most powerful univariate classifiers for an optimal choice of the quantile level. A simple aggregation approach for constructing a multivariate classifier based upon these component‐wise distances to the within‐class quantiles is proposed. It is shown that this classifier is consistent with the asymptotically optimal classifier as the sample size increases. Our proposed classifiers result in simple piecewise‐linear decision rule boundaries that can be efficiently trained. Numerical results are shown to demonstrate competitive performance for the proposed classifiers on both simulated data and a benchmark email spam application. 

   more » « less