A New Goodness-of-Fit Test for Azzalini’s Skew-t Distribution Based on the Energy Distance Framework with Applications
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| Title: | A New Goodness-of-Fit Test for Azzalini’s Skew-t Distribution Based on the Energy Distance Framework with Applications |
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| Authors: | Njuki, Joseph, Hasan, Abeer M. |
| Source: | Mathematics and Statistics |
| Publisher Information: | CCU Digital Commons |
| Publication Year: | 2025 |
| Collection: | Coastal Carolina University: CCU Digital Commons |
| Subject Terms: | Skew-t distribution, Goodness-of-Fit (GOF), energy statistics, skewness, Empirical Distribution Function (EDF), Probability Density Function (PDF), Maximum Likelihood Estimation (MLE), Cumulative Density Function (CDF), Akaike information criterion (AIC), Schwarz information criterion (SIC), Mathematics |
| Description: | In response to the growing need for flexible parametric models for skewed and heavy-tailed data, this paper introduces a novel goodness-of-fit test for the Skew-t distribution, a widely used flexible parametric probability distribution. Traditional methods often fail to capture the complex behavior of data in fields such as engineering, public health, and the social sciences. Our proposed test, based on energy statistics, provides practitioners with a robust and powerful tool for assessing the suitability of the Skew-t distribution for their data. We present a comprehensive methodological evaluation, including a comparative study that highlights the advantages of our approach over traditional tests. The results of our simulation studies demonstrate a significant improvement in power, leading to more reliable inference. To further showcase the practical utility of our method, we apply the proposed test to three real-world datasets, offering a valuable contribution to both the theoretical and applied aspects of statistical modeling for non-normal data. This article was published Open Access through the CCU Libraries Open Access Publishing Fund. The article was first published in the journal Mathematics: https://doi.org/10.3390/math13233833 |
| Document Type: | text |
| File Description: | application/pdf |
| Language: | unknown |
| Availability: | https://digitalcommons.coastal.edu/mathematics-statistics/5 https://digitalcommons.coastal.edu/context/mathematics-statistics/article/1004/viewcontent/njuki_2025_a_new_goodness_of_fit_test_mathematics_13_03833_accessible.pdf |
| Rights: | http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: | edsbas.EA79A1EB |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | In response to the growing need for flexible parametric models for skewed and heavy-tailed data, this paper introduces a novel goodness-of-fit test for the Skew-t distribution, a widely used flexible parametric probability distribution. Traditional methods often fail to capture the complex behavior of data in fields such as engineering, public health, and the social sciences. Our proposed test, based on energy statistics, provides practitioners with a robust and powerful tool for assessing the suitability of the Skew-t distribution for their data. We present a comprehensive methodological evaluation, including a comparative study that highlights the advantages of our approach over traditional tests. The results of our simulation studies demonstrate a significant improvement in power, leading to more reliable inference. To further showcase the practical utility of our method, we apply the proposed test to three real-world datasets, offering a valuable contribution to both the theoretical and applied aspects of statistical modeling for non-normal data. This article was published Open Access through the CCU Libraries Open Access Publishing Fund. The article was first published in the journal Mathematics: https://doi.org/10.3390/math13233833 |
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