Posterior Contraction and Credible Sets for Filaments of Regression Functions

TL;DR

This paper introduces a Bayesian method for estimating filaments in regression functions using B-splines, providing theoretical contraction rates and credible sets with good coverage, demonstrated through simulations and earthquake data.

Contribution

It develops a Bayesian approach to filament estimation in regression, offering improved theoretical contraction rates and credible sets with valid frequentist coverage.

Findings

Posterior contraction rate is (n/log n)^{(2-α)/(2(1+α))} for α ≥ 4.

Method achieves better bias control compared to kernel methods.

Credible sets have sufficient frequentist coverage, validated through simulations and real data.

Abstract

A filament consists of local maximizers of a smooth function $f$ when moving in a certain direction. A filamentary structure is an important feature of the shape of an object and is also considered as an important lower dimensional characterization of multivariate data. There have been some recent theoretical studies of filaments in the nonparametric kernel density estimation context. This paper supplements the current literature in two ways. First, we provide a Bayesian approach to the filament estimation in regression context and study the posterior contraction rates using a finite random series of B-splines basis. Compared with the kernel-estimation method, this has a theoretical advantage as the bias can be better controlled when the function is smoother, which allows obtaining better rates. Assuming that $f: \mathbb{R}^2 \mapsto \mathbb{R}$ belongs to an isotropic H\"{o}lder class of order $\alpha \geq 4$, with the optimal choice of smoothing parameters, the posterior contraction rates for the filament points on some appropriately defined integral curves and for the Hausdorff distance of the filament are both $(n/\log n)^{(2-\alpha)/(2(1+\alpha))}$. Secondly, we provide a way to construct a credible set with sufficient frequentist coverage for the filaments. We demonstrate the success of our proposed method in simulations and one application to earthquake data.