Model-based statistical depth with applications to functional data

Weilong Zhao, Zishen Xu, Yue Mu, Yun Yang, Wei Wu

Research output: Contribution to journalArticlepeer-review


Statistical depth, a commonly used analytic tool in nonparametric statistics, has been extensively studied for multivariate and functional observations over the past few decades. Although various forms of depth were introduced, they are mainly procedure based whose definitions are independent of the generative model for observations. To address this problem, we introduce a generative model-based approach to define statistical depth for both multivariate and functional data. The proposed model-based depth framework permits simple computation via a bootstrap sampling and improves the depth estimation accuracy. When applied to functional data, the proposed depth can capture important features such as continuity, smoothness or phase variability, depending on the defining criteria. We propose efficient algorithms to compute the proposed depths and establish estimation consistency. Through simulations and real data, we demonstrate that the proposed functional depths reveal important statistical information such as those captured by the median and quantiles, and detect outliers.

Original languageEnglish (US)
Pages (from-to)313-356
Number of pages44
JournalJournal of Nonparametric Statistics
Issue number2
StatePublished - 2024


  • Model-based
  • functional data
  • reproducing kernel Hilbert space
  • statistical depth
  • stochastic process

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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