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July 29, 2026Stat

Quantile Tensor Factor Regression With Interaction Effects and Its Application to Multimodal Data Analysis

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Authors

PPPengfei PiSLShan Luo

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Overview

Randomized trial demonstrates improved predictive performance in multimodal data analysis, suggesting better evaluation techniques for ADHD.

Key Points

  • This research introduces a quantile regression method designed for multimodal data using tensor covariates.
  • Developed a novel quantile tensor factor regression method incorporating interaction effects.
  • Utilized simulations to validate statistical accuracy and stability of the method.
  • Applied the method to ADHD-200 data for performance comparison against alternative models.
  • Achieved superior predictive performance compared to state-of-the-art methods in ADHD data analysis.
  • Demonstrated greater computational efficiency as indicated by performance metrics.

Cite This Study

Pi et al. (2026) studied this question.

synapsesocial.com/papers/6a69a26dc8da07d9defa5bfdhttps://doi.org/10.1002/sta4.70168
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Also Consider

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  5. 5Tensor quantile regression with low-rank tensor train estimation2024 · 1 citations