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Ren, H., Zou, C., & Li, R. (2022). Extrapolation-based Tuning Parameters Selection in Massive Data Analysis. SCIENTIA SINICA Mathematica, 52(6), 689-.Search in Google Scholar
Murtagh, F. (2017). Massive Data Clustering in Moderate Dimensions from the Dual Spaces of Observation and Attribute Data Clouds.Search in Google Scholar
Zhu, R. (2015). Poisson Subsampling Algorithms for Large Sample Linear Regression in Massive Data. Stats.Search in Google Scholar
Pan, R., Zhu, Y., Guo, B., et al. (2021). A Sequential Addressing Subsampling Method for Massive Data Analysis under Memory Constraint. arXiv e-prints.Search in Google Scholar
Zhao, Y. (2018). Feasible Algorithm for Linear Mixed Model for Massive Data. Communications in Statistics, B. Simulation and Computation.Search in Google Scholar
Jang, W., Kim, G., & Kim, J. (2016). Current Trends in High Dimensional Massive Data Analysis. Korean Journal of Applied Statistics, 29(6), 999-1005.Search in Google Scholar
Jx, A., Mh, B., Wl, C., et al. (2020). Fused Variable Screening for Massive Imbalanced Data. Computational Statistics & Data Analysis, 141, 94-108.Search in Google Scholar
Yong, W. U., & Liu, L. Q. (2016). Pyramid Statistical Method Based on Massive Data. Metallurgical Industry Automation.Search in Google Scholar
Zhao, J. B., Liu, Y. X., Liu, N., et al. (2019). Spatial Prediction Method of Regional Landslide Based on Distributed BP Neural Network Algorithm under Massive Monitoring Data. Rock and Soil Mechanics.Search in Google Scholar
Corbin, Q., Christian, F., Daniel, T., et al. (2018). emeraLD: Rapid Linkage Disequilibrium Estimation with Massive Data Sets. Bioinformatics, 1.Search in Google Scholar
Chen, A. C. (2015). Method for Transmitting Massive Data by Using Dynamically Adjusted Updating Frequencies.Search in Google Scholar
Dekel, O., Gilad-Bachrach, R., Shamir, O., et al. (2010). Optimal Distributed Online Prediction using Mini-Batches. Journal of Machine Learning Research, 13(1), 165-202.Search in Google Scholar
Zinkevich, M., Weimer, M., Li, L., Smola, A. J. (2010). Parallelized Stochastic Gradient Descent. Neural Information Processing Systems, 2595-2603.Search in Google Scholar
Boyd, S., Parikh, N., Chu, E., et al. (2010). Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations & Trends in Machine Learning, 3(1), 1-122.Search in Google Scholar
Kw, A., Slb, C. (2021). Robust Distributed Modal Regression for Massive Data. Computational Statistics & Data Analysis.Search in Google Scholar
Zhao, T., Cheng, G., Liu, H. (2016). A Partially Linear Framework for Massive Heterogeneous Data. Annals of Statistics, 44(4), 1400-1437.Search in Google Scholar
Datta, A., Banerjee, S., Finley, A. O., et al. (2016). On Nearest-Neighbor Gaussian Process Models for Massive Spatial Data. Wires Computational Statistics, 8(5), 162-171.Search in Google Scholar
Fang, F., Yin, X., Zhang, Q., et al. (2018). Divide and Conquer Algorithms for Model Averaging with Massive Data. Journal of Systems Science and Mathematical Sciences.Search in Google Scholar
Si, Y., Heeringa, S., Johnson, D., et al. (2021). Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics. Journal of Survey Statistics and Methodology.Search in Google Scholar
Cheng, G., Zhao, et al. (2016). A Partially Linear Framework for Massive Heterogeneous Data. The Annals of Statistics: An Official Journal of the Institute of Mathematical Statistics, 44(4), 1400-1437.Search in Google Scholar
Bu, L. Z., Zhao, W., & Wang, W. (2019). Second order hierarchical partial least squares regression-polynomial chaos expansion for global sensitivity and reliability analyses of high-dimensional models.Search in Google Scholar
[22] Huang, B., & Ma, C. (2018). An iterative algorithm for the least Frobenius norm least squares solution of a class of generalized coupled Sylvester-transpose linear matrix equations. Applied Mathematics and Computation, 328, 58-74.Search in Google Scholar