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- W4317435087 abstract "Outliers often occur during data collection, which could impact the result seriously and lead to a large inference error; therefore, it is important to detect outliers before data analysis. Gamma distribution is a popular distribution in statistics; this paper proposes a method for detecting multiple upper outliers from gamma (m,θ). For computing the critical value of the test statistic in our method, we derive the density function for the case of a single outlier and design two algorithms based on the Monte Carlo and the kernel density estimation for the case of multiple upper outliers. A simulation study shows that the test statistic proposed in this paper outperforms some common test statistics. Finally, we propose an improved testing method to reduce the impact of the swamping effect, which is demonstrated by real data analyses." @default.
- W4317435087 created "2023-01-19" @default.
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- W4317435087 date "2023-01-19" @default.
- W4317435087 modified "2023-09-26" @default.
- W4317435087 title "A Method for Detecting Outliers from the Gamma Distribution" @default.
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- W4317435087 doi "https://doi.org/10.3390/axioms12020107" @default.
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