# Testing High-dimensional Multinomials with Applications to Text Analysis

arXiv: https://arxiv.org/abs/2301.01381 · HTML: https://mlsanity.com/paper/2301.01381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 3 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [Optimal Algorithms for Testing Closeness of Discrete Distributions](https://mlsanity.com/paper/1308.3946/) (2013)
- [Network Global Testing by Counting Graphlets](https://mlsanity.com/paper/1807.08440/) (2018)

## Built on by (most cited first, 1 of 1)

- [Token-Efficient Change Detection in LLM APIs](https://mlsanity.com/paper/2602.11083/) (2026)

## Abstract (arXiv)

Motivated by applications in text mining and discrete distribution inference, we investigate the testing for equality of probability mass functions of K groups of high-dimensional multinomial distributions. A test statistic, which is shown to have an asymptotic standard normal distribution under the null, is proposed. The optimal detection boundary is established, and the proposed test is shown to achieve this optimal detection boundary across the entire parameter space of interest. The proposed method is demonstrated in simulation studies and applied to analyze two real-world datasets to examine variation among consumer reviews of Amazon movies and diversity of statistical paper abstracts.