On Metric Choice in Dimension Reduction for Fréchet Regression Article Swipe
Abdul‐Nasah Soale
,
Cuiqing Ma
,
Siyu Chen
,
Obed Koomson
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/insr.12615
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/insr.12615
Summary Fréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data. Fréchet regression utilises the pairwise distances between the random objects, which makes the choice of metric crucial in the estimation. In this paper, existing dimension reduction methods for Fréchet regression are reviewed, and the effect of metric choice on the estimation of the dimension reduction subspace is explored for the regression between random responses and Euclidean predictors. An extensive numerical study illustrate how different metrics affect the central and central mean space estimators. Two real applications involving analysis of brain connectivity networks of subjects with and without Parkinson's disease and an analysis of the distributions of glycaemia based on continuous glucose monitoring data are provided, to demonstrate how metric choice can influence findings in real applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/insr.12615
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/insr.12615
- OA Status
- hybrid
- Cited By
- 1
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410135795
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4410135795Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/insr.12615Digital Object Identifier
- Title
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On Metric Choice in Dimension Reduction for Fréchet RegressionWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-05Full publication date if available
- Authors
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Abdul‐Nasah Soale, Cuiqing Ma, Siyu Chen, Obed KoomsonList of authors in order
- Landing page
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https://doi.org/10.1111/insr.12615Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/insr.12615Direct link to full text PDF
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/insr.12615Direct OA link when available
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Mathematics, Sliced inverse regression, Statistics, Metric (unit), Dimension (graph theory), Dimensionality reduction, Reduction (mathematics), Sufficient dimension reduction, Regression, Combinatorics, Artificial intelligence, Computer science, Economics, Geometry, Operations managementTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.study | 102 |
| abstract_inverted_index.types | 15 |
| abstract_inverted_index.which | 52 |
| abstract_inverted_index.affect | 107 |
| abstract_inverted_index.choice | 55, 79, 151 |
| abstract_inverted_index.effect | 76 |
| abstract_inverted_index.health | 33 |
| abstract_inverted_index.method | 24 |
| abstract_inverted_index.metric | 19, 57, 78, 150 |
| abstract_inverted_index.modern | 8 |
| abstract_inverted_index.paper, | 64 |
| abstract_inverted_index.random | 50, 94 |
| abstract_inverted_index.useful | 27 |
| abstract_inverted_index.Summary | 0 |
| abstract_inverted_index.between | 48, 93 |
| abstract_inverted_index.central | 109, 111 |
| abstract_inverted_index.complex | 32 |
| abstract_inverted_index.crucial | 58 |
| abstract_inverted_index.disease | 130 |
| abstract_inverted_index.general | 18 |
| abstract_inverted_index.glucose | 142 |
| abstract_inverted_index.imaging | 40 |
| abstract_inverted_index.methods | 68 |
| abstract_inverted_index.metrics | 106 |
| abstract_inverted_index.spaces. | 20 |
| abstract_inverted_index.without | 128 |
| abstract_inverted_index.Fréchet | 1, 42, 70 |
| abstract_inverted_index.analysis | 10, 30, 119, 133 |
| abstract_inverted_index.becoming | 4 |
| abstract_inverted_index.existing | 65 |
| abstract_inverted_index.explored | 89 |
| abstract_inverted_index.findings | 154 |
| abstract_inverted_index.mainstay | 6 |
| abstract_inverted_index.networks | 123 |
| abstract_inverted_index.objects, | 51 |
| abstract_inverted_index.pairwise | 46 |
| abstract_inverted_index.subjects | 125 |
| abstract_inverted_index.subspace | 87 |
| abstract_inverted_index.utilises | 44 |
| abstract_inverted_index.Euclidean | 97 |
| abstract_inverted_index.analysing | 12 |
| abstract_inverted_index.belonging | 16 |
| abstract_inverted_index.different | 105 |
| abstract_inverted_index.dimension | 66, 85 |
| abstract_inverted_index.distances | 47 |
| abstract_inverted_index.extensive | 100 |
| abstract_inverted_index.glycaemia | 138 |
| abstract_inverted_index.influence | 153 |
| abstract_inverted_index.involving | 118 |
| abstract_inverted_index.numerical | 101 |
| abstract_inverted_index.provided, | 146 |
| abstract_inverted_index.reduction | 67, 86 |
| abstract_inverted_index.responses | 95 |
| abstract_inverted_index.reviewed, | 73 |
| abstract_inverted_index.continuous | 37, 141 |
| abstract_inverted_index.especially | 26 |
| abstract_inverted_index.estimation | 82 |
| abstract_inverted_index.illustrate | 103 |
| abstract_inverted_index.monitoring | 38, 143 |
| abstract_inverted_index.regression | 2, 23, 43, 71, 92 |
| abstract_inverted_index.Parkinson's | 129 |
| abstract_inverted_index.demonstrate | 148 |
| abstract_inverted_index.estimation. | 61 |
| abstract_inverted_index.estimators. | 114 |
| abstract_inverted_index.predictors. | 98 |
| abstract_inverted_index.applications | 117 |
| abstract_inverted_index.connectivity | 122 |
| abstract_inverted_index.applications. | 157 |
| abstract_inverted_index.distributions | 136 |
| abstract_inverted_index.non‐traditional | 13 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.18443795 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |