FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2065604017> ?p ?o ?g. }

• W2065604017 endingPage "117" @default.
• W2065604017 startingPage "92" @default.
• W2065604017 abstract "Multimodal fusion is becoming more common as it proves to be a powerful approach to identify complementary information from multimodal datasets. However, simulation of joint information is not straightforward. Published approaches mostly employ limited, provisional designs that often break the link between the model assumptions and the data for the sake of demonstrating properties of fusion techniques. This work introduces a new approach to synthetic data generation which allows full-compliance between data and model while still representing realistic spatiotemporal features in accordance with the current neuroimaging literature. The focus is on the simulation of joint information for the verification of stochastic linear models, particularly those used in multimodal data fusion of brain imaging data. Our first goal is to obtain a benchmark ground-truth in which estimation errors due to model mismatch are minimal or none. Then we move on to assess how estimation is affected by gradually increasing model discrepancies toward a more realistic dataset. The key aspect of our approach is that it permits complete control over the type and level of model mismatch, allowing for more educated inferences about the limitations and caveats of select stochastic linear models. As a result, impartial comparison of models is possible based on their performance in multiple different scenarios. Our proposed method uses the commonly overlooked theory of copulas to enable full control of the type and level of dependence/association between modalities, with no occurrence of spurious multimodal associations. Moreover, our approach allows for arbitrary single-modality marginal distributions for any fixed choice of dependence/association between multimodal features. Using our simulation framework, we can rigorously challenge the assumptions of several existing multimodal fusion approaches. Our study brings a new perspective to the problem of simulating multimodal data that can be used for ground-truth verification of various stochastic multimodal models available in the literature, and reveals some important aspects that are not captured or are overlooked by ad hoc simulations that lack a firm statistical motivation." @default.
• W2065604017 created "2016-06-24" @default.
• W2065604017 creator A5024260846 @default.
• W2065604017 creator A5032850756 @default.
• W2065604017 creator A5060798483 @default.
• W2065604017 creator A5082230429 @default.
• W2065604017 date "2014-11-01" @default.
• W2065604017 modified "2023-10-16" @default.
• W2065604017 title "A statistically motivated framework for simulation of stochastic data fusion models applied to multimodal neuroimaging" @default.
• W2065604017 cites W1978161337 @default.
• W2065604017 cites W1984990856 @default.
• W2065604017 cites W1985327120 @default.
• W2065604017 cites W1992315244 @default.
• W2065604017 cites W2003449088 @default.
• W2065604017 cites W2008591007 @default.
• W2065604017 cites W2012557400 @default.
• W2065604017 cites W2016444985 @default.
• W2065604017 cites W2018575471 @default.
• W2065604017 cites W2021189660 @default.
• W2065604017 cites W2023095826 @default.
• W2065604017 cites W2033128599 @default.
• W2065604017 cites W2035128299 @default.
• W2065604017 cites W2058408209 @default.
• W2065604017 cites W2065131965 @default.
• W2065604017 cites W2078604986 @default.
• W2065604017 cites W2082207932 @default.
• W2065604017 cites W2084147096 @default.
• W2065604017 cites W2090664364 @default.
• W2065604017 cites W2096052879 @default.
• W2065604017 cites W2098787985 @default.
• W2065604017 cites W2113082700 @default.
• W2065604017 cites W2115971978 @default.
• W2065604017 cites W2127958806 @default.
• W2065604017 cites W2131436045 @default.
• W2065604017 cites W2131617561 @default.
• W2065604017 cites W2137911531 @default.
• W2065604017 cites W2154053567 @default.
• W2065604017 cites W4238398947 @default.
• W2065604017 doi "https://doi.org/10.1016/j.neuroimage.2014.04.035" @default.
• W2065604017 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7733398" @default.
• W2065604017 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24747087" @default.
• W2065604017 hasPublicationYear "2014" @default.
• W2065604017 type Work @default.
• W2065604017 sameAs 2065604017 @default.
• W2065604017 citedByCount "10" @default.
• W2065604017 countsByYear W20656040172014 @default.
• W2065604017 countsByYear W20656040172015 @default.
• W2065604017 countsByYear W20656040172018 @default.
• W2065604017 countsByYear W20656040172019 @default.
• W2065604017 countsByYear W20656040172021 @default.
• W2065604017 countsByYear W20656040172022 @default.
• W2065604017 countsByYear W20656040172023 @default.
• W2065604017 crossrefType "journal-article" @default.
• W2065604017 hasAuthorship W2065604017A5024260846 @default.
• W2065604017 hasAuthorship W2065604017A5032850756 @default.
• W2065604017 hasAuthorship W2065604017A5060798483 @default.
• W2065604017 hasAuthorship W2065604017A5082230429 @default.
• W2065604017 hasBestOaLocation W20656040172 @default.
• W2065604017 hasConcept C118552586 @default.
• W2065604017 hasConcept C119857082 @default.
• W2065604017 hasConcept C120665830 @default.
• W2065604017 hasConcept C121332964 @default.
• W2065604017 hasConcept C124101348 @default.
• W2065604017 hasConcept C13280743 @default.
• W2065604017 hasConcept C146849305 @default.
• W2065604017 hasConcept C154945302 @default.
• W2065604017 hasConcept C15744967 @default.
• W2065604017 hasConcept C185798385 @default.
• W2065604017 hasConcept C192209626 @default.
• W2065604017 hasConcept C205649164 @default.
• W2065604017 hasConcept C33954974 @default.
• W2065604017 hasConcept C41008148 @default.
• W2065604017 hasConcept C58693492 @default.
• W2065604017 hasConcept C97256817 @default.
• W2065604017 hasConceptScore W2065604017C118552586 @default.
• W2065604017 hasConceptScore W2065604017C119857082 @default.
• W2065604017 hasConceptScore W2065604017C120665830 @default.
• W2065604017 hasConceptScore W2065604017C121332964 @default.
• W2065604017 hasConceptScore W2065604017C124101348 @default.
• W2065604017 hasConceptScore W2065604017C13280743 @default.
• W2065604017 hasConceptScore W2065604017C146849305 @default.
• W2065604017 hasConceptScore W2065604017C154945302 @default.
• W2065604017 hasConceptScore W2065604017C15744967 @default.
• W2065604017 hasConceptScore W2065604017C185798385 @default.
• W2065604017 hasConceptScore W2065604017C192209626 @default.
• W2065604017 hasConceptScore W2065604017C205649164 @default.
• W2065604017 hasConceptScore W2065604017C33954974 @default.
• W2065604017 hasConceptScore W2065604017C41008148 @default.
• W2065604017 hasConceptScore W2065604017C58693492 @default.
• W2065604017 hasConceptScore W2065604017C97256817 @default.
• W2065604017 hasFunder F4320332161 @default.
• W2065604017 hasLocation W20656040171 @default.
• W2065604017 hasLocation W20656040172 @default.
• W2065604017 hasLocation W20656040173 @default.
• W2065604017 hasOpenAccess W2065604017 @default.
• W2065604017 hasPrimaryLocation W20656040171 @default.
• W2065604017 hasRelatedWork W112744582 @default.
• W2065604017 hasRelatedWork W1485630101 @default.

• next