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March 10, 2026Wiley Interdisciplinary Reviews Computational Statistics

Advances in Modal Regression: From Theoretical Foundations to Practical Implementations

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Authors

SXSijia XiangWYWeixin YaoXCXinping Cui

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Overview

This review highlights modal regression's robustness in estimation, predicting modes across various fields, suggesting further avenues for research.

Key Points

  • This work aims to summarize the development and implementation of modal regression as an alternative to traditional regression approaches.
  • Synthesized theoretical developments in modal regression.
  • Reviewed computational algorithms applicable to modal regression.
  • Discussed applications in diverse fields like finance and public health.
  • Highlighted key challenges and potential directions for future research.
  • Modal regression offers significant robustness against outliers and skewed data.
  • It provides more informative predictive intervals compared to traditional methods.
  • The review identifies advancements in parametric, semiparametric, and nonparametric modalities.

Cite This Study

Xiang et al. (2026) studied this question.

synapsesocial.com/papers/69af954870916d39fea4caa5https://doi.org/10.1002/wics.70061
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