Optimal subsampling for large‐sample quantile regression with massive data

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Optimal subsampling for functional quantile regression

Cites Work

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Subsampling inference in threshold autoregressive models
Local case-control sampling: efficient subsampling in imbalanced data sets
Faster least squares approximation
On the uniform asymptotic validity of subsampling and the bootstrap
Some asymptotic results for jackknifing the sample quantile
Censored regression quantiles
Some asymptotic theory for the bootstrap
Bootstrap methods: another look at the jackknife
Subsampling
The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. With comments by Ronald A. Thisted and M. R. Osborne and a rejoinder by the authors
A general theory for jackknife variance estimation
Large sample confidence regions based on subsamples under minimal assumptions
Optimal subsampling for large-scale quantile regression
Quantile regression under memory constraint
Quantile regression in big data: a divide and conquer based strategy
Distributed inference for quantile regression processes
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Efficient resampling methods for nonsmooth estimating functions
Sampling algorithms for l₂ regression and applications
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A Simple Method for Robust Regression
Regression Quantiles
Censored Regression Quantiles
A Lack-of-Fit Test for Quantile Regression
Robust, Scalable, and Fast Bootstrap Method for Analyzing Large Scale Data
Optimal Subsampling for Large Sample Logistic Regression
A Scalable Bootstrap for Massive Data
Information-Based Optimal Subdata Selection for Big Data Linear Regression
Optimal Design of Experiments
Efficient Quantile Regression Analysis With Missing Observations
Subspace embeddings for the L ₁ -norm with applications
Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure
Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
Optimal subsampling for quantile regression in big data
First-Order Newton-Type Estimator for Distributed Estimation and Inference