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Optimal subsampling for quantile regression in big data

https://doi.org/10.1093/biomet/asaa043
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Mathematical Methods for Physical and Analytical Chemistry
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Domain-Adversarial Training of Neural Networks
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Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring
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Quantile Regression
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Online Updating of Statistical Inference in the Big Data Setting
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Optimal Subsampling for Large Sample Logistic Regression
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Information-Based Optimal Subdata Selection for Big Data Linear Regression
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Challenges of Big Data analysis
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Optimum Experimental Designs, with SAS
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On the de la Garza phenomenon
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Distributed testing and estimation under sparse high dimensional models
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Distributed inference for quantile regression processes
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quantreg: Quantile Regression
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R: A Language and Environment for Statistical Computing
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Aggregated estimating equation estimation
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A Statistical Perspective on Algorithmic Leveraging
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Fast approximation of matrix coherence and statistical leverage
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A statistical perspective on randomized sketching for ordinary least-squares
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OPTIMAL SUBSAMPLING ALGORITHMS FOR BIG DATA REGRESSIONS
inferredcites →https://doi.org/10.6084/m9.figshare.5787525openalex-snapshot
Information-Based Optimal Subdata Selection for Big Data Linear Regression
inferredcites →https://doi.org/10.6084/m9.figshare.6453071openalex-snapshot
Optimal Subsampling for Large Sample Logistic Regression

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Maximum sampled conditional likelihood for informative subsampling
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Functional L-Optimality Subsampling for Massive Data
inferred← citeshttps://doi.org/10.48550/arxiv.2110.13048openalex-snapshot
Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
inferred← citeshttps://doi.org/10.48550/arxiv.2111.03173openalex-snapshot
Optimal pooling and distributed inference for the tail index and extreme quantiles
inferred← citeshttps://doi.org/10.51387/23-nejsds36openalex-snapshot
Subdata Selection With a Large Number of Variables
inferred← citeshttps://doi.org/10.52933/jdssv.v4i3.78openalex-snapshot
Subdata selection for big data regression: an improved approach
inferred← citeshttps://doi.org/10.5705/ss.202022.0020openalex-snapshot
A Perturbation Subsampling for Large Scale Data
inferred← citeshttps://doi.org/10.5705/ss.202022.0277openalex-snapshot
Optimal Subsampling for Multinomial Logistic Models With Big Data
inferred← citeshttps://doi.org/10.5705/ss.202023.0124openalex-snapshot
Leverage Classifier: Another Look at Support Vector Machine
inferred← citeshttps://doi.org/10.5705/ss.202023.0274openalex-snapshot
Nearly Optimal Two-step Poisson Sampling and Empirical Likelihood Weighting Estimation for M-estimation with Big Data
inferred← citeshttps://doi.org/10.6339/21-jds999openalex-snapshot
A Review on Optimal Subsampling Methods for Massive Datasets
inferred← citeshttps://doi.org/10.6339/22-jds1057openalex-snapshot
Sampling-based Gaussian Mixture Regression for Big Data
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