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Article summary:
1. The article discusses two-sample tests of high dimensional mean vectors via random integration.
2. It examines existing methods that assume equal covariance matrices and introduces new methods that do not require this assumption.
3. It also categorizes the various methods according to their assumptions and requirements.
Article analysis:
The article is generally trustworthy and reliable, as it provides a comprehensive overview of existing two-sample tests for high dimensional mean vectors via random integration, as well as introducing new methods that do not require the assumption of equal covariance matrices. The article is well-researched and provides detailed explanations of each method, including its assumptions and requirements. Furthermore, the article does not appear to be biased or one-sided in its reporting, as it presents both sides equally and does not make any unsupported claims or omit any points of consideration. Additionally, the article does not contain any promotional content or partiality towards any particular method or approach. Finally, the article does note possible risks associated with each method, such as power loss in certain scenarios. In conclusion, this article is a reliable source of information on two-sample tests for high dimensional mean vectors via random integration.