Projection Test for Mean Vector in High Dimensions
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an online projection test for high-dimensional data, improving statistical power and maintaining accuracy. The novel method effectively analyzes complex datasets by projecting them into lower dimensions.
Area Of Science
- Statistics
- High-dimensional data analysis
Background
- Traditional statistical methods struggle with high-dimensional data.
- Projection tests reduce dimensionality for easier analysis.
Purpose Of The Study
- To develop a powerful and accurate projection test for high-dimensional mean vectors.
- To address power loss issues in existing data-splitting projection methods.
Main Methods
- Proposed a novel estimation for optimal projection direction using constrained quadratic programming.
- Developed two tests: one with data-splitting (exact t-test under normality) and an online version.
- The online framework iteratively updates projection direction estimates with new data.
Main Results
- The online-style projection test asymptotically converges to a standard normal distribution.
- Simulations and real-data analysis demonstrate the proposed test maintains type I error rates.
- The online test shows superior statistical power compared to existing methods.
Conclusions
- The proposed online projection test is a robust and powerful tool for high-dimensional mean vector analysis.
- This method offers a significant advancement over traditional and data-splitting approaches.
- Effective for both theoretical statistical research and practical data analysis applications.
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