English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper proposes a new deep-learning method to construct test statisticsby computer vision and metrics learning. The application highlighted in thispaper is applying computer vision on Q-Q plot to construct a new test statisticfor normality test. To the best of our knowledge, there is no similarapplication documented in the literature. Traditionally, there are two familiesof approaches for verifying the probability distribution of a random variable.Researchers either subjectively assess the Q-Q plot or objectively use amathematical formula, such as Kolmogorov-Smirnov test, to formally conduct anormality test. Graphical assessment by human beings is not rigorous whereasnormality test statistics may not be accurate enough when the uniformly mostpowerful test does not exist. It may take tens of years for statistician todevelop a new test statistic that is more powerful statistically. Our proposedmethod integrates four components based on deep learning: an imagerepresentation learning component of a Q-Q plot, a dimension reductioncomponent, a metrics learning component that best quantifies the differencesbetween two Q-Q plots for normality test, and a new normality hypothesistesting process. Our experimentation results show that themachine-learning-based test statistics can outperform several widely-usedtraditional normality tests. This study provides convincing evidence that theproposed method could objectively create a powerful test statistic based on Q-Qplots and this method could be modified to construct many more powerful teststatistics for other applications in the future.

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