Another Test of Association for Count Data
Fast Zero-Tolerant Pearson's Chi-squared Test of Association
Fast Zero-Tolerant G-Test of Association
Fast Upsilon Test of Association between Two Categorical Variables
Zero-Tolerant Pearson's Chi-squared Statistic
Zero-Tolerant Pearson's Chi-squared Test for Contingency Tables
Zero-Tolerant G-Test for Contingency Tables
Plot Matrix with Entries Represented by Balloons of Varying Sizes and ...
Recover Raw Data Vectors from Contingency Table
Upsilon Goodness-of-Fit Test Statistic
Upsilon Goodness-of-Fit Test for Count Data
Upsilon Test Statistic for Contingency Tables
Upsilon Test of Association for Count Data
The Upsilon test assesses association among categorical variables against the null hypothesis of independence (Luo 2021 MS thesis; ProQuest Publication No. 28649813). While promoting dominant function patterns, it demotes non-dominant function patterns. It is robust to low expected count---continuity correction like Yates's seems unnecessary. Using a common null population following a uniform distribution, contingency tables are comparable by statistical significance---not the case for most association tests defining a varying null population by tensor product of observed marginals. Although Pearson's chi-squared test, Fisher's exact test, and Woolf's G-test (related to mutual information) are useful in some contexts, the Upsilon test appeals to ranking association patterns not necessarily following same marginal distributions, such as in count data from DNA sequencing---an important modern scientific domain.