Covariance Matrix Estimation for High-Dimensional Interval-Valued Data with Positive Definiteness

2026-04-01

AI Summary

This study addresses the lack of a unified, positive-definite covariance matrix estimator for high-dimensional interval-valued data. Assuming that the upper and lower bounds of intervals share a common dependence structure, the authors propose the first positive-definite covariance estimation framework by extending the classical soft-thresholding method to develop an Interval-valued Soft Thresholding (IST) estimator. A positive-definiteness constraint is explicitly incorporated, and the resulting optimization problem is efficiently solved via the Alternating Direction Method of Multipliers. Theoretically, non-asymptotic error bounds for the IST estimator are established. Empirical evaluations demonstrate that the proposed method achieves superior estimation accuracy and practical efficacy, both in high-dimensional simulations and on real-world high-frequency financial data from the CSI 300 index.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Matrix & Tensor Methods
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

In the realm of high-dimensional data analysis, the estimation of covariance matrices is a fundamental task, and this holds true for interval-valued data as well. However, there is no unified definition for the covariance matrix of interval-valued data, let alone established estimation methods in high-dimensional settings. This paper presents a novel approach to estimating covariance matrices for high-dimensional interval-valued data while ensuring positive definiteness. We begin by assuming that the upper and lower bounds of interval-valued variables share the same dependency structure. Based on this assumption, we extend the classical soft-thresholding covariance matrix estimator to the interval-valued scenario, referred to as the Interval-valued Soft-Thresholding (IST) estimator. Subsequently, to ensure the positive definiteness of the estimator, we impose a positive definiteness constraint on the IST estimator. We derive an alternating direction method to solve the proposed problem and establish its convergence. Under some very mild conditions, we develop a non-asymptotic statistical theory for the proposed estimator. Simulation studies and applications to high-frequency financial data from the CSI 300 Index demonstrated the effectiveness of the proposed estimator.

Problem

• Covariance Matrix Estimation
• High-Dimensional Data
• Interval-Valued Data
• Positive Definiteness

Innovation

• interval-valued data
• covariance matrix estimation
• positive definiteness
• soft-thresholding
• high-dimensional statistics