Monotone Curve Estimation via Convex Duality

TL;DR

This paper introduces a new monotone principal curve estimation method based on optimal transport and convex duality, with theoretical guarantees and practical advantages for data with monotonic structures.

Contribution

It develops a novel monotone curve estimation framework with rigorous statistical guarantees and demonstrates its effectiveness through simulations and real-world data applications.

Findings

Outperforms existing methods in accuracy for monotonic data

Provides statistical guarantees including mean squared error bounds

Shows robustness in noisy, complex real-world datasets

Abstract

A principal curve serves as a powerful tool for uncovering underlying structures of data through 1-dimensional smooth and continuous representations. On the basis of optimal transport theories, this paper introduces a novel principal curve framework constrained by monotonicity with rigorous theoretical justifications. We establish statistical guarantees for our monotone curve estimate, including expected empirical and generalized mean squared errors, while proving the existence of such estimates. These statistical foundations justify adopting the popular early stopping procedure in machine learning to implement our numeric algorithm with neural networks. Comprehensive simulation studies reveal that the proposed monotone curve estimate outperforms competing methods in terms of accuracy when the data exhibits a monotonic structure. Moreover, through two real-world applications on future prices of copper, gold, and silver, and avocado prices and sales volume, we underline the robustness of our curve estimate against variable transformation, further confirming its effective applicability for noisy and complex data sets. We believe that this monotone curve-fitting framework offers significant potential for numerous applications where monotonic relationships are intrinsic or need to be imposed.