Distributed quantile regression for longitudinal big data
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Publication:6567424
DOI10.1007/S00180-022-01318-0MaRDI QIDQ6567424
Ye Fan, Author name not available (Why is that?), Nan Lin
Publication date: 5 July 2024
Published in: Computational Statistics (Search for Journal in Brave)
Cites Work
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- Longitudinal data analysis using generalized linear models
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Improving generalised estimating equations using quadratic inference functions
- Linear quantile regression models for longitudinal experiments: an overview
- Empirical likelihood for quantile regression models with longitudinal data
- Quantile regression for longitudinal data with a working correlation model
- Smoothing combined estimating equations in quantile regression for longitudinal data
- Improving estimation efficiency in quantile regression with longitudinal data
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- 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
- An alternating direction method of multipliers for MCP-penalized regression with high-dimensional data
- Composite quantile regression for correlated data
- Parallel multi-block ADMM with \(o(1/k)\) convergence
- Quantile regression for longitudinal data
- Optimal subsampling for large-scale quantile regression
- Quantile regression under memory constraint
- Quantile regression in big data: a divide and conquer based strategy
- Weighted quantile regression for longitudinal data using empirical likelihood
- Distributed inference for quantile regression processes
- Quantile Regression for Large-Scale Applications
- Empirical likelihood and quantile regression in longitudinal data analysis
- Quantile regression for longitudinal data using the asymmetric Laplace distribution
- Regression Quantiles
- An ADMM with continuation algorithm for non-convex SICA-penalized regression in high dimensions
- Standard errors and covariance matrices for smoothed rank estimators
- On first-order algorithms forl1/nuclear norm minimization
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Optimal subsampling for quantile regression in big data
- The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
- ADMM for Penalized Quantile Regression in Big Data
- ADMM for High-Dimensional Sparse Penalized Quantile Regression
- Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout, with applications to quality of life measurements from clinical trials
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