Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Browsing by Author Jiang, R
Showing results 1 to 13 of 13
| Issue Date | Title | Author(s) |
| 2026 | A communication-efficient distributed Retire with application to the analysis of multi-site air-quality distributed data | Yu, K; Jiang, R; Wang, J |
| 3-Sep-2018 | Composite quantile regression for massive datasets | Jiang, R; Hu, X; Yu, K; Qian, W |
| 14-Nov-2019 | Learning Spectral and Spatial Features Based on Generative Adversarial Network for Hyperspectral Image Super-Resolution | Jiang, R; Li, X; Gao, A; Li, L; Meng, H; Yue, S; Zhang, L |
| 3-Dec-2021 | No-crossing single-index quantile regression curve estimation | Yu, K; Jiang, R |
| 11-Oct-2023 | Non-crossing quantile double-autoregression for the analysis of streaming time series data | Jiang, R; Choy, SK; Yu, K |
| 23-Feb-2024 | Renewable Huber estimation method for streaming datasets | Jiang, R; Liang, L.; Yu, K |
| 13-Aug-2022 | Renewable quantile regression for streaming data sets | Jiang, R; Yu, K |
| 9-Oct-2023 | Rong Jiang and Keming Yu's Discussion of “Estimating means of bounded random variables by betting” by Ian Waudby-Smith and Aaditya Ramdas | Jiang, R; Yu, K |
| 2020 | Single-index composite quantile regression for massive data | Yu, K; Jiang, R |
| 4-Aug-2020 | Single-index expectile models for estimating conditional value at risk and expected shortfall | Jiang, R; Hu, X; Yu, K |
| 27-Aug-2021 | Smoothing quantile regression for a distributed system | Yu, K; Jiang, R |
| 5-Jan-2024 | Unconditional Quantile Regression for Streaming Datasets | Jiang, R; Yu, K |
| 28-Jul-2018 | Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples | Jiang, R; Yu, K; Zhang, T |
