Academic Journal
Navigating interpretability and alpha control in GF-KCSD testing with measurement error: A Kernel approach
| Title: | Navigating interpretability and alpha control in GF-KCSD testing with measurement error: A Kernel approach |
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| Authors: | Elham Afzali, Saman Muthukumarana, Liqun Wang |
| Source: | Machine Learning with Applications, Vol 17, Iss , Pp 100581- (2024) |
| Publisher Information: | Elsevier, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Cybernetics LCC:Electronic computers. Computer science |
| Subject Terms: | Gradient-free Kernel conditional stein discrepancy (GF-KCSD), Bootstrap resampling, Measurement error, Goodness-of-fit testing, Brain MRI data analysis, Conditional distributions, Cybernetics, Q300-390, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: | The Gradient-Free Kernel Conditional Stein Discrepancy (GF-KCSD), presented in our prior work, represents a significant advancement in goodness-of-fit testing for conditional distributions. This method offers a robust alternative to previous gradient-based techniques, specially when the gradient calculation is intractable or computationally expensive. In this study, we explore previously unexamined aspects of GF-KCSD, with a particular focus on critical values and test power—essential components for effective hypothesis testing. We also present novel investigation on the impact of measurement errors on the performance of GF-KCSD in comparison to established benchmarks, enhancing our understanding of its resilience to these errors. Through controlled experiments using synthetic data, we demonstrate GF-KCSD’s superior ability to control type-I error rates and maintain high statistical power, even in the presence of measurement inaccuracies. Our empirical evaluation extends to real-world datasets, including brain MRI data. The findings confirm that GF-KCSD performs comparably to KCSD in hypothesis testing effectiveness while requiring significantly less computational time. This demonstrates GF-KCSD’s capability as an efficient tool for analyzing complex data, enhancing its value for scenarios that demand rapid and robust statistical analysis. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2666-8270 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666827024000574; https://doaj.org/toc/2666-8270 |
| DOI: | 10.1016/j.mlwa.2024.100581 |
| Access URL: | https://doaj.org/article/14cdf448635d499a8b5b4ba74607c173 |
| Accession Number: | edsdoj.14cdf448635d499a8b5b4ba74607c173 |
| Database: | Directory of Open Access Journals |
| ISSN: | 26668270 |
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| DOI: | 10.1016/j.mlwa.2024.100581 |
| Published in: | Machine Learning with Applications |
| Language: | English |