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June 14, 2026Journal of Computational and Graphical Statistics

Tensor Additive Quantile Regression

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Authors

WLWenqi LuYZYu ZhangZZZhongyi Zhu

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Overview

Randomized trial demonstrates robust variable selection in tensor data, suggesting improved analysis methods.

Key Points

  • This research aims to develop a tensor additive quantile regression model for analyzing tensor data effectively.
  • Introduced a tensor additive quantile regression model using basis function approximations.
  • Employed Tucker decomposition for dimension reduction and to connect sparse tensor elements.
  • Evaluated the model's performance through Monte Carlo simulations and real-world data applications.
  • Demonstrated improved variable selection over oracle methods with a broader set of relevant features.
  • Established strong large sample properties of the proposed estimators.
  • Showcased efficiency in handling high-dimensional settings in applications such as stock market data.

Cite This Study

Lu et al. (2026) studied this question.

synapsesocial.com/papers/6a2e47cdb1cc60ccdea8c2e5https://doi.org/10.1080/10618600.2026.2687761
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