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Robust functional multivariate analysis of variance with environmental applications

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  • Zhuo Qu
  • Wenlin Dai
  • Marc G. Genton

Abstract

We propose median polish for functional multivariate analysis of variance (FMANOVA) with the implementation of depth for multivariate functional data. As an alternative to classical mean estimation, functional median polish estimates the functional grand effect and factor effects based on functional medians in one‐way and two‐way additive FMANOVA models. Median polish estimates in FMANOVA are visually unbiased, independently of the choice of multivariate functional depth. The corresponding mean‐based and rank‐based tests are generalized to evaluate whether the functional medians in various levels of the factors are the same. Simulation studies illustrate the robustness of our functional median polish in various scenarios, compared with the results from classical FMANOVA fitted by means. The results are evaluated both marginally and jointly. Three environmental data sets are considered to illustrate that our median polish is robust against outliers in practical implementations. Functional boxplots and heat maps are two ways of visualizing the functional factors, depending on whether the functional data are curves or images, respectively.

Suggested Citation

  • Zhuo Qu & Wenlin Dai & Marc G. Genton, 2021. "Robust functional multivariate analysis of variance with environmental applications," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.
    • Handle: RePEc:wly:envmet:v:32:y:2021:i:1:n:e2641
    DOI: 10.1002/env.2641
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    References listed on IDEAS

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    2. Trevor Harris & Bo Li & J. Derek Tucker, 2022. "Scalable multiple changepoint detection for functional data sequences," Environmetrics, John Wiley & Sons, Ltd., vol. 33(2), March.

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