Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation-Reference-Cited by-同舟云学术

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Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation

Published:2020-01-23 Issue:535 Volume:116 Page:1307-1318
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Shi Chengchun¹,Song Rui²,Lu Wenbin²,Li Runze³^ORCID

Affiliation:

1. Department of Statistics, London School of Economics and Political Science, London , UK;;

2. Department of Statistics, North Carolina State University , Raleigh , NC;;

3. Department of Statistics, The Pennsylvania State University, University Park , PA

Funder

Division of Mathematical Sciences

National Cancer Institute

NSF

NIH

NNSFC

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2019.1710154

Reference27 articles.

1. One-Step Huber Estimates in the Linear Model

2. Simultaneous analysis of Lasso and Dantzig selector

3. Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection

4. High-dimensional variable screening and bias in subsequent inference, with an empirical comparison

5. The Dantzig selector: Statistical estimation when p is much larger than n

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1. Online debiased lasso estimation and inference for heterogenous updating regressions;Journal of the Korean Statistical Society;2024-07-19

2. Inference for possibly misspecified generalized linear models with nonpolynomial-dimensional nuisance parameters;Biometrika;2024-05-24

3. Online updating mode learning for streaming datasets;Journal of Statistical Computation and Simulation;2024-05-15

4. Renewable Huber estimation method for streaming datasets;Electronic Journal of Statistics;2024-01-01

5. Bayesian quantile regression for streaming data;AIMS Mathematics;2024

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