Model-based Statistical Depth with Applications to Functional Data

TL;DR

This paper introduces a model-based statistical depth framework for multivariate and functional data, improving estimation accuracy and capturing key features like continuity and phase variability.

Contribution

It develops a generative model-based depth definition for functional data using covariance eigensystems, with efficient algorithms and consistency results.

Findings

Enhanced depth estimation accuracy

Effective outlier detection in functional data

Captures important features like continuity and phase variability

Abstract

Statistical depth, a commonly used analytic tool in non-parametric 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 Monte Carlo 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. Specifically, we view functional data as realizations from a second-order stochastic process, and define their depths through the eigensystem of the covariance operator. These new definitions are given through a proper metric related to the reproducing kernel Hilbert space of the covariance operator. 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.