Advanced Distribution Theory for Significance in Scale Space

TL;DR

This paper develops a new advanced distribution theory for Significance in Scale Space (SSS), enabling fully valid hypothesis testing for detecting slopes and curvatures in noisy 2-D image data across multiple scales.

Contribution

It introduces a novel probability methodology that provides a valid inference framework for 2-D SSS, overcoming previous limitations due to complex dependence structures.

Findings

Controls Type I error in noise images

Maintains high power in detecting true signals

Successfully identifies features in gamma camera images

Abstract

Smoothing methods find signals in noisy data. A challenge for Statistical inference is the choice of smoothing parameter. SiZer addressed this challenge in one-dimension by detecting significant slopes across multiple scales, but was not a completely valid testing procedure. This was addressed by the development of an advanced distribution theory that ensures fully valid inference in the 1-D setting by applying extreme value theory. A two-dimensional extension of SiZer, known as Significance in Scale Space (SSS), was developed for image data, enabling the detection of both slopes and curvatures across multiple spatial scales. However, fully valid inference for 2-D SSS has remained unavailable, largely due to the more complex dependence structure of random fields. In this paper, we use a completely different probability methodology which gives an advanced distribution theory for SSS, establishing a valid hypothesis testing procedure for both slope and curvature detection. When applied to pure noise images (no true underlying signal), the proposed method controls the Type I error, whereas the original SSS identifies spurious features across scales. When signal is present, the proposed method maintains a high level of statistical power, successfully identifying important true slopes and curvatures in real data such as gamma camera images.