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Identification of outlying observations for large-dimensional data
Tao Wang1, Xiaona Yang2, Yunfei Guo3,4
1School of Mathematics and Statistics, Huaiyin Normal University, Huaian City, People's Republic of China.
Journal of Applied Statistics
|January 26, 2023
Summary
This study introduces a novel two-stage method for detecting outliers in large datasets. The procedure effectively identifies unusual data points, offering improved accuracy and reliability for data analysis.
Area of Science:
- Statistics
- Data Mining
- Machine Learning
Background:
- Identifying outlying observations is crucial for robust data analysis.
- Existing methods may struggle with large-dimensional datasets.
Purpose of the Study:
- To propose an effective two-stage procedure for outlier identification in large-dimensional data.
- To develop a refined algorithm for enhanced outlier detection performance.
Main Methods:
- A two-stage procedure utilizing a max-normal statistic and a clean subset.
- Exploration of asymptotic distribution for threshold determination.
- Development of a one-step refined algorithm to improve identification power.
Main Results:
- The proposed method demonstrates significant advantages in outlier identification.
- Effective control over misjudgment rates was achieved.
- Validated through simulations and real-world data analysis.
Conclusions:
- The novel two-stage procedure offers a powerful and reliable approach to outlier detection.
- The refined algorithm enhances the practical applicability for large datasets.
Keywords:
Outlier identificationasymptotic distributionlarge-dimension statisticsmultiple hypothesis testingrobust statisticsMore Related Videos
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