A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis-Reference-Cited by-同舟云学术

A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis

Published:2020-04-02 Issue:534 Volume:116 Page:805-818
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Hector Emily C.¹^ORCID,Song Peter X.-K.¹

Affiliation:

1. Department of Biostatistics, University of Michigan, Ann Arbor, MI

Funder

Division of Mathematical Sciences

National Institute of Environmental Health Sciences

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2020.1736082

Reference49 articles.

1. Pairwise likelihood inference for spatial regressions estimated on very large datasets

2. Joint composite estimating functions in spatiotemporal models

3. Gaussian predictive process models for large spatial data sets

4. Battey, H., Fan, J., Liu, H., Lu, J., and Zhu, Z. (2015), “Distributed Estimation and Inference With Statistical Guarantees,” arXiv no. 1509.05457.

5. Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Distributed Regression Analysis Application Package Using SAS;Statistics in Biosciences;2024-07-09

2. A selective review on statistical methods for massive data computation: distributed computing, subsampling, and minibatch techniques;Statistical Theory and Related Fields;2024-04-23

3. A Randomized Pairwise Likelihood Method for Complex Statistical Inferences;Journal of the American Statistical Association;2023-09-12

4. Quasi-Newton updating for large-scale distributed learning;Journal of the Royal Statistical Society Series B: Statistical Methodology;2023-06-10

5. Distributed Inference for Spatial Extremes Modeling in High Dimensions;Journal of the American Statistical Association;2023-04-13