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- Publisher Website: 10.1214/18-EJS1466
- Scopus: eid_2-s2.0-85063394455
- PMID: 31666911
- WOS: WOS:000460450800052
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Article: Heterogeneity adjustment with applications to graphical model inference
Title | Heterogeneity adjustment with applications to graphical model inference |
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Authors | |
Keywords | Brain image network Multiple sourcing Semiparametric factor model Principal component analysis Batch effect |
Issue Date | 2018 |
Citation | Electronic Journal of Statistics, 2018, v. 12, n. 2, p. 3908-3952 How to Cite? |
Abstract | Heterogeneity is an unwanted variation when analyzing aggregated datasets from multiple sources. Though different methods have been proposed for heterogeneity adjustment, no systematic theory exists to justify these methods. In this work, we propose a generic framework named ALPHA (short for Adaptive Low-rank Principal Heterogeneity Adjustment) to model, estimate, and adjust heterogeneity from the original data. Once the heterogeneity is adjusted, we are able to remove the batch effects and to enhance the inferential power by aggregating the homogeneous residuals from multiple sources. Under a pervasive assumption that the latent heterogeneity factors simultaneously affect a fraction of observed variables, we provide a rigorous theory to justify the proposed framework. Our framework also allows the incorporation of informative covariates and appeals to the ‘Bless of Dimensionality’. As an illustrative application of this generic framework, we consider a problem of estimating high-dimensional precision matrix for graphical model inference based on multiple datasets. We also provide thorough numerical studies on both synthetic datasets and a brain imaging dataset to demonstrate the efficacy of the developed theory and methods. |
Persistent Identifier | http://hdl.handle.net/10722/303603 |
ISSN | 2023 Impact Factor: 1.0 2023 SCImago Journal Rankings: 1.256 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Fan, Jianqing | - |
dc.contributor.author | Liu, Han | - |
dc.contributor.author | Wang, Weichen | - |
dc.contributor.author | Zhu, Ziwei | - |
dc.date.accessioned | 2021-09-15T08:25:39Z | - |
dc.date.available | 2021-09-15T08:25:39Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Electronic Journal of Statistics, 2018, v. 12, n. 2, p. 3908-3952 | - |
dc.identifier.issn | 1935-7524 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303603 | - |
dc.description.abstract | Heterogeneity is an unwanted variation when analyzing aggregated datasets from multiple sources. Though different methods have been proposed for heterogeneity adjustment, no systematic theory exists to justify these methods. In this work, we propose a generic framework named ALPHA (short for Adaptive Low-rank Principal Heterogeneity Adjustment) to model, estimate, and adjust heterogeneity from the original data. Once the heterogeneity is adjusted, we are able to remove the batch effects and to enhance the inferential power by aggregating the homogeneous residuals from multiple sources. Under a pervasive assumption that the latent heterogeneity factors simultaneously affect a fraction of observed variables, we provide a rigorous theory to justify the proposed framework. Our framework also allows the incorporation of informative covariates and appeals to the ‘Bless of Dimensionality’. As an illustrative application of this generic framework, we consider a problem of estimating high-dimensional precision matrix for graphical model inference based on multiple datasets. We also provide thorough numerical studies on both synthetic datasets and a brain imaging dataset to demonstrate the efficacy of the developed theory and methods. | - |
dc.language | eng | - |
dc.relation.ispartof | Electronic Journal of Statistics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Brain image network | - |
dc.subject | Multiple sourcing | - |
dc.subject | Semiparametric factor model | - |
dc.subject | Principal component analysis | - |
dc.subject | Batch effect | - |
dc.title | Heterogeneity adjustment with applications to graphical model inference | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1214/18-EJS1466 | - |
dc.identifier.pmid | 31666911 | - |
dc.identifier.pmcid | PMC6820685 | - |
dc.identifier.scopus | eid_2-s2.0-85063394455 | - |
dc.identifier.volume | 12 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 3908 | - |
dc.identifier.epage | 3952 | - |
dc.identifier.isi | WOS:000460450800052 | - |