Distance Correlation in Multiple Biased Sampling Models Article Swipe
Ke Yan
,
Hok Kan Ling
,
Yanglei Song
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.11808
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.11808
Testing the independence between random vectors is a fundamental problem in statistics. Distance correlation, a recently popular dependence measure, is universally consistent for testing independence against all distributions with finite moments. However, when data are subject to selection bias or collected from multiple sources or schemes, spurious dependence may arise. This creates a need for methods that can effectively utilize data from different sources and correct these biases. In this paper, we study the estimation of distance covariance and distance correlation under multiple biased sampling models, which provide a natural framework for addressing these issues. Theoretical properties, including the strong consistency and asymptotic null distributions of the distance covariance and correlation estimators, and the rate at which the test statistic diverges under sequences of alternatives approaching the null, are established. A weighted permutation procedure is proposed to determine the critical value of the independence test. Simulation studies demonstrate that our approach improves both the estimation of distance correlation and the power of the test.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.11808
- https://arxiv.org/pdf/2408.11808
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405419030
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405419030Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2408.11808Digital Object Identifier
- Title
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Distance Correlation in Multiple Biased Sampling ModelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-21Full publication date if available
- Authors
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Ke Yan, Hok Kan Ling, Yanglei SongList of authors in order
- Landing page
-
https://arxiv.org/abs/2408.11808Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2408.11808Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2408.11808Direct OA link when available
- Concepts
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Correlation, Distance sampling, Sampling (signal processing), Statistics, Mathematics, Statistical physics, Computer science, Physics, Geometry, Biology, Computer vision, Abundance (ecology), Fishery, Filter (signal processing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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