Optimal subsampling algorithms for composite quantile regression in massive data
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Publication:6132705
DOI10.1080/02331888.2023.2239507OpenAlexW4385246867MaRDI QIDQ6132705
Jun Jin, Shuangzhe Liu, Tie-Feng Ma
Publication date: 17 August 2023
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331888.2023.2239507
asymptotic distributioncomposite quantile regressionmassive dataoptimal subsamplingcombining subsamples
Cites Work
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- Estimation of linear composite quantile regression using EM algorithm
- Aggregated estimating equation estimation
- Estimation and test procedures for composite quantile regression with covariates missing at random
- Faster least squares approximation
- Composite quantile regression and the oracle model selection theory
- On the de la Garza phenomenon
- Distributed testing and estimation under sparse high dimensional models
- Statistical methods and computing for big data
- Two step composite quantile regression for single-index models
- Limiting distributions for \(L_1\) regression estimators under general conditions
- Penalized weighted composite quantile regression for partially linear varying coefficient models with missing covariates
- Optimal subsampling for large-scale quantile regression
- Randomized sketches for kernels: fast and optimal nonparametric regression
- Adaptive iterative Hessian sketch via \(A\)-optimal subsampling
- Quantile regression in big data: a divide and conquer based strategy
- Distributed inference for quantile regression processes
- Optimal subsampling for softmax regression
- New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
- Low-Rank Approximation and Regression in Input Sparsity Time
- Sampling algorithms for l2 regression and applications
- Composite quantile regression for massive datasets
- Optimal Subsampling for Large Sample Logistic Regression
- A Scalable Bootstrap for Massive Data
- A note on the efficiency of composite quantile regression
- Information-Based Optimal Subdata Selection for Big Data Linear Regression
- Communication-Efficient Distributed Statistical Inference
- Speeding Up MCMC by Efficient Data Subsampling
- Optimal Design of Experiments
- Optimal subsampling for quantile regression in big data
- Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data