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Tensor Regression with Applications in Neuroimaging Data Analysis

Citations

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Cited by:

  1. Lin Liu, 2021. "Matrix‐based introduction to multivariate data analysis, by KoheiAdachi 2nd edition. Singapore: Springer Nature, 2020. pp. 457," Biometrics, The International Biometric Society, vol. 77(4), pages 1498-1500, December.
  2. Will Wei Sun & Junwei Lu & Han Liu & Guang Cheng, 2017. "Provable sparse tensor decomposition," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 899-916, June.
  3. Cui Guo & Jian Kang & Timothy D. Johnson, 2022. "A spatial Bayesian latent factor model for image‐on‐image regression," Biometrics, The International Biometric Society, vol. 78(1), pages 72-84, March.
  4. Wang, Lei & Zhang, Jing & Li, Bo & Liu, Xiaohui, 2022. "Quantile trace regression via nuclear norm regularization," Statistics & Probability Letters, Elsevier, vol. 182(C).
  5. Bo Wei & Limin Peng & Ying Guo & Amita Manatunga & Jennifer Stevens, 2023. "Tensor response quantile regression with neuroimaging data," Biometrics, The International Biometric Society, vol. 79(3), pages 1947-1958, September.
  6. Chen, Rong & Xiao, Han & Yang, Dan, 2021. "Autoregressive models for matrix-valued time series," Journal of Econometrics, Elsevier, vol. 222(1), pages 539-560.
  7. Vivek F. Farias & Andrew A. L, 2019. "Learning Preferences with Side Information," Management Science, INFORMS, vol. 65(7), pages 3131-3149, July.
  8. Yao Lei Xu & Kriton Konstantinidis & Danilo P. Mandic, 2022. "Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling," Papers 2211.05581, arXiv.org.
  9. Rungang Han & Yuetian Luo & Miaoyan Wang & Anru R. Zhang, 2022. "Exact clustering in tensor block model: Statistical optimality and computational limit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1666-1698, November.
  10. Andrea Bucci, 2022. "A smooth transition autoregressive model for matrix-variate time series," Papers 2212.08615, arXiv.org.
  11. Kenneth W. Latimer & David J. Freedman, 2023. "Low-dimensional encoding of decisions in parietal cortex reflects long-term training history," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
  12. Xiaoshan Li & Da Xu & Hua Zhou & Lexin Li, 2018. "Tucker Tensor Regression and Neuroimaging Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 520-545, December.
  13. Pluta, Dustin & Yu, Zhaoxia & Shen, Tong & Chen, Chuansheng & Xue, Gui & Ombao, Hernando, 2018. "Statistical methods and challenges in connectome genetics," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 83-86.
  14. Hayato Maki & Sakriani Sakti & Hiroki Tanaka & Satoshi Nakamura, 2018. "Quality prediction of synthesized speech based on tensor structured EEG signals," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
  15. Zhao, Junlong & Niu, Lu & Zhan, Shushi, 2017. "Trace regression model with simultaneously low rank and row(column) sparse parameter," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 1-18.
  16. Jade Xiaoqing Wang & Yimei Li & Wilburn E. Reddick & Heather M. Conklin & John O. Glass & Arzu Onar‐Thomas & Amar Gajjar & Cheng Cheng & Zhao‐Hua Lu, 2023. "A high‐dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer," Biometrics, The International Biometric Society, vol. 79(3), pages 2430-2443, September.
  17. Kim, Jonathan & Sandri, Brian J. & Rao, Raghavendra B. & Lock, Eric F., 2023. "Bayesian predictive modeling of multi-source multi-way data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
  18. Guhaniyogi, Rajarshi, 2017. "Convergence rate of Bayesian supervised tensor modeling with multiway shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 157-168.
  19. Giuseppe Brandi & T. Di Matteo, 2020. "A new multilayer network construction via Tensor learning," Papers 2004.05367, arXiv.org.
  20. Monica Billio & Roberto Casarin & Matteo Iacopini & Sylvia Kaufmann, 2023. "Bayesian Dynamic Tensor Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 429-439, April.
  21. Chelsey Hill & James Li & Matthew J. Schneider & Martin T. Wells, 2021. "The tensor auto‐regressive model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 636-652, July.
  22. Inkoo Lee & Debajyoti Sinha & Qing Mai & Xin Zhang & Dipankar Bandyopadhyay, 2023. "Bayesian regression analysis of skewed tensor responses," Biometrics, The International Biometric Society, vol. 79(3), pages 1814-1825, September.
  23. Ghannam, Mai & Nkurunziza, Sévérien, 2023. "Tensor Stein-rules in a generalized tensor regression model," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
  24. Zhu, Xuehu & Guo, Xu & Wang, Tao & Zhu, Lixing, 2020. "Dimensionality determination: A thresholding double ridge ratio approach," Computational Statistics & Data Analysis, Elsevier, vol. 146(C).
  25. Zengchao Xu & Shan Luo & Zehua Chen, 2023. "A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 441-467, February.
  26. Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.
  27. Hongtu Zhu & Dan Shen & Xuewei Peng & Leo Yufeng Liu, 2017. "MWPCR: Multiscale Weighted Principal Component Regression for High-Dimensional Prediction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1009-1021, July.
  28. Florian Gunsilius, 2020. "Distributional synthetic controls," Papers 2001.06118, arXiv.org, revised Dec 2021.
  29. Kai Deng & Xin Zhang, 2022. "Tensor envelope mixture model for simultaneous clustering and multiway dimension reduction," Biometrics, The International Biometric Society, vol. 78(3), pages 1067-1079, September.
  30. Ke, Baofang & Zhao, Weihua & Wang, Lei, 2023. "Smoothed tensor quantile regression estimation for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
  31. Philip T. Reiss & Jeff Goldsmith & Han Lin Shang & R. Todd Ogden, 2017. "Methods for Scalar-on-Function Regression," International Statistical Review, International Statistical Institute, vol. 85(2), pages 228-249, August.
  32. Lan Liu & Wei Li & Zhihua Su & Dennis Cook & Luca Vizioli & Essa Yacoub, 2022. "Efficient estimation via envelope chain in magnetic resonance imaging‐based studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 481-501, June.
  33. Wendi Bao & Feiyu Zhang & Weiguo Li & Qin Wang & Ying Gao, 2022. "Randomized Average Kaczmarz Algorithm for Tensor Linear Systems," Mathematics, MDPI, vol. 10(23), pages 1-24, December.
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