Modal regression using kernel density estimation: A review

We review recent advances in modal regression studies using kernel density estimation. Modal regression is an alternative approach for investigating the relationship between a response variable and its covariates. Specifically, modal regression summarizes the interactions between the response variable and covariates using the conditional mode or local modes. We first describe the underlying model of modal regression and its estimators based on kernel density estimation. We then review the asymptotic properties of the estimators and strategies for choosing the smoothing bandwidth. We also discuss useful algorithms and similar alternative approaches for modal regression, and propose future direction in this field.

• Publication year

  2017

• Venue

  Wiley Interdisciplinary Reviews: Computational Statistics

• Publication date

  2017-10-18

• Fields of study

  Mathematics, Computer Science, Economics

• Identifiers
  DOI 10.1002/wics.1431arXiv 1710.07004
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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