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Editorial

Editorial to the special issue: Statistical Approaches for Big Data and Machine Learning

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References

  • L. Zhang, T. Zhu, and J.T. Zhang, Two-sample Behrens–Fisher problems for high-dimensional data: A normal reference scale-invariant test. J. Appl. Stat. 50 (2023), pp. 456–476.
  • D. Pustokhin, I. Pustokhina, P. Dinh, S. Phan, G. Nguyen, G. Joshi and, and K. Shankar, An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19. J. Appl. Stat. 50 (2023), pp. 477–494.
  • Mi. Yuan and Q. Wen, A practical two-sample test for weighted random graphs. J. Appl. Stat. 50 (2023), pp. 495–511.
  • M. Zhao, X. Xu, Y. Zhu, K. Zhang, and Y. Zhou, Model estimation and selection for partial linear varying coefficient EV models with longitudinal data. J. Appl. Stat. 50 (2023), pp. 512–534.
  • M. Weber, J. Striaukas, M. Schumacher, and H. Binder, Regularized regression when covariates are linked on a network: The 3CoSE algorithm. J. Appl. Stat. 50 (2023), pp. 535–554.
  • X. Xie, J. Shi, and K. Song, A distributed multiple sample testing for massive data. J. Appl. Stat. 50 (2023), pp. 555–573.
  • A. Thielmann, C. Weisser, A. Krenz, and B. Säfken, Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling. J. Appl. Stat. 50 (2023), pp. 574–591.
  • F. Fan, S.-C. Chu, J.-S. Pan, C. Lin, and H. Zhao, An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks. J. Appl. Stat. 50 (2023), pp. 592–609.
  • M. Zhang Wu, J. Luo, X. Fang, M. Xu, and P. Zhao, Modeling multivariate cyber risks: Deep learning dating extreme value theory. J. Appl. Stat. 50 (2023), pp. 610–630.
  • W. Zhang, C.O. Wu, X. Ma, X. Tian, and Q. Li, Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies. J. Appl. Stat. 50 (2023), pp. 631–658.
  • X. Zhi, T. Yu, L. Bi, and Y. Li, Noise-insensitive discriminative subspace fuzzy clustering. J. Appl. Stat. 50 (2023), pp. 659–674.
  • I. Kang, C. Park, Y.J. Yoon, C. Park, S.-S. Kwon, and H. Choi, Classification of histogram-valued data with support histogram machines. J. Appl. Stat. 50 (2023), pp. 675–690.
  • X. Liu, G. Tian, and Z. Liu, Identification of novel genes for triple-negative breast cancer with semiparametric gene-based analysis. J. Appl. Stat. 50 (2023), pp. 691–702.
  • X. Zhi, J. Liu, S. Wu, and C. Niu, A generalized l2,p-norm regression based feature selection algorithm. J. Appl. Stat. 50 (2023), pp. 703–723.
  • Y. Cheng, Y. Li, M.L. Smith, C. Li, and Y. Shen, Analyzing evidence-based falls prevention data with significant missing information using variable selection after multiple imputation. J. Appl. Stat. 50 (2023), pp. 724–743.
  • M. Zhou, and W. Yao, Sensitivity analysis of unmeasured confounding in causal inference based on exponential tilting and super learner. J. Appl. Stat. 50 (2023), pp. 744–760.
  • M. Dagdoug, C. Goga, and D. Haziza, Model-assisted estimation in high-dimensional settings for survey data. J. Appl. Stat. 50 (2023), pp. 761–785.
  • S. Chen, and C. Xu, Handling high-dimensional data with missing values by modern machine learning techniques. J. Appl. Stat. 50 (2023), pp. 786–804.
  • J. Jin, L. Zhang, E. Leng, G.J. Metzger, and J.S. Koopmeiners, Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI. J. Appl. Stat. 50 (2023), pp. 805–826.

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