Communication-efficient distributed estimator for generalized linear models with a diverging number of covariates
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Publication:830480
DOI10.1016/j.csda.2020.107154OpenAlexW3111080575WikidataQ112880909 ScholiaQ112880909MaRDI QIDQ830480
Publication date: 7 May 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2001.06194
asymptotic efficiencygeneralized linear modelsdiverging \(p\)large-scale distributed dataone-step MLE
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Cites Work
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- Sure independence screening in generalized linear models with NP-dimensionality
- Aggregated estimating equation estimation
- The spectrum of kernel random matrices
- Asymptotic normality and strong consistency of maximum quasi-likelihood estimates in generalized linear models
- On M-processes and M-estimation
- Asymptotic behavior of M estimators of p regression parameters when \(p^ 2/n\) is large. II: Normal approximation
- Limiting behavior of \(M\)-estimators of regression coefficients in high dimensional linear models. I: Scale-dependent case. II: Scale-invariant case
- Strong consistency of maximum quasi-likelihood estimators in generalized linear models with fixed and adaptive designs
- On parameters of increasing dimensions
- A distributed one-step estimator
- Robust regression: Asymptotics, conjectures and Monte Carlo
- Nonconcave penalized likelihood with a diverging number of parameters.
- Just interpolate: kernel ``ridgeless regression can generalize
- GEE analysis of clustered binary data with diverging number of covariates
- A split-and-conquer approach for analysis of
- Mathematical Statistics
- Optimal Subsampling for Large Sample Logistic Regression
- Communication-Efficient Distributed Statistical Inference
- Tests for High Dimensional Generalized Linear Models
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