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A Nonparametric Regression Model With Tree-Structured Response

Author

Listed:
  • Yuan Wang
  • J. S. Marron
  • Burcu Aydin
  • Alim Ladha
  • Elizabeth Bullitt
  • Haonan Wang

Abstract

Developments in science and technology over the last two decades has motivated the study of complex data objects. In this article, we consider the topological properties of a population of tree-structured objects. Our interest centers on modeling the relationship between a tree-structured response and other covariates. For tree-structured objects, this poses serious challenges since most regression methods rely on linear operations in Euclidean space. We generalize the notion of nonparametric regression to the case of a tree-structured response variable. In addition, we develop a fast algorithm and give its theoretical justification. We implement the proposed method to analyze a dataset of human brain artery trees. An important lesson is that smoothing in the full tree space can reveal much deeper scientific insights than the simple smoothing of summary statistics. This article has supplementary materials online.

Suggested Citation

  • Yuan Wang & J. S. Marron & Burcu Aydin & Alim Ladha & Elizabeth Bullitt & Haonan Wang, 2012. "A Nonparametric Regression Model With Tree-Structured Response," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1272-1285, December.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:500:p:1272-1285
    DOI: 10.1080/01621459.2012.699348
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    Cited by:

    1. Karthik Bharath & Prabhanjan Kambadur & Dipak. K. Dey & Arvind Rao & Veerabhadran Baladandayuthapani, 2017. "Statistical Tests for Large Tree-Structured Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1733-1743, October.
    2. Xiaosun Lu & J. S. Marron & Perry Haaland, 2014. "Object-Oriented Data Analysis of Cell Images," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 548-559, June.

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