Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test

TL;DR

This paper introduces a novel deep learning approach that uses computer vision on Q-Q plots to create a new, more powerful normality test statistic, outperforming traditional methods.

Contribution

The paper presents the first application of deep learning to analyze Q-Q plots for normality testing, integrating multiple components to improve test power.

Findings

Machine-learning-based test outperforms traditional normality tests

Deep learning effectively captures Q-Q plot features for hypothesis testing

Proposed method offers a new framework for constructing powerful test statistics

Abstract

This paper proposes a new deep-learning method to construct test statistics by computer vision and metrics learning. The application highlighted in this paper is applying computer vision on Q-Q plot to construct a new test statistic for normality test. To the best of our knowledge, there is no similar application documented in the literature. Traditionally, there are two families of approaches for verifying the probability distribution of a random variable. Researchers either subjectively assess the Q-Q plot or objectively use a mathematical formula, such as Kolmogorov-Smirnov test, to formally conduct a normality test. Graphical assessment by human beings is not rigorous whereas normality test statistics may not be accurate enough when the uniformly most powerful test does not exist. It may take tens of years for statistician to develop a new test statistic that is more powerful statistically. Our proposed method integrates four components based on deep learning: an image representation learning component of a Q-Q plot, a dimension reduction component, a metrics learning component that best quantifies the differences between two Q-Q plots for normality test, and a new normality hypothesis testing process. Our experimentation results show that the machine-learning-based test statistics can outperform several widely-used traditional normality tests. This study provides convincing evidence that the proposed method could objectively create a powerful test statistic based on Q-Q plots and this method could be modified to construct many more powerful test statistics for other applications in the future.