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- Publisher Website: 10.1111/biom.12885
- Scopus: eid_2-s2.0-85046539137
- PMID: 29738627
- WOS: WOS:000457779100017
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Article: Exponential Family Functional data analysis via a low‐rank model
Title | Exponential Family Functional data analysis via a low‐rank model |
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Authors | |
Keywords | Functional principal component analysis Generalized linear model Mortality study Singular value decomposition Two‐way functional data |
Issue Date | 2018 |
Publisher | Wiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1541-0420 |
Citation | Biometrics, 2018, v. 74 n. 4, p. 1301-1310 How to Cite? |
Abstract | In many applications, non‐Gaussian data such as binary or count are observed over a continuous domain and there exists a smooth underlying structure for describing such data. We develop a new functional data method to deal with this kind of data when the data are regularly spaced on the continuous domain. Our method, referred to as Exponential Family Functional Principal Component Analysis (EFPCA), assumes the data are generated from an exponential family distribution, and the matrix of the canonical parameters has a low‐rank structure. The proposed method flexibly accommodates not only the standard one‐way functional data, but also two‐way (or bivariate) functional data. In addition, we introduce a new cross validation method for estimating the latent rank of a generalized data matrix. We demonstrate the efficacy of the proposed methods using a comprehensive simulation study. The proposed method is also applied to a real application of the UK mortality study, where data are binomially distributed and two‐way functional across age groups and calendar years. The results offer novel insights into the underlying mortality pattern. |
Persistent Identifier | http://hdl.handle.net/10722/278557 |
ISSN | 2023 Impact Factor: 1.4 2023 SCImago Journal Rankings: 1.480 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, G | - |
dc.contributor.author | Huang, JZ | - |
dc.contributor.author | Shen, H | - |
dc.date.accessioned | 2019-10-21T02:09:44Z | - |
dc.date.available | 2019-10-21T02:09:44Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Biometrics, 2018, v. 74 n. 4, p. 1301-1310 | - |
dc.identifier.issn | 0006-341X | - |
dc.identifier.uri | http://hdl.handle.net/10722/278557 | - |
dc.description.abstract | In many applications, non‐Gaussian data such as binary or count are observed over a continuous domain and there exists a smooth underlying structure for describing such data. We develop a new functional data method to deal with this kind of data when the data are regularly spaced on the continuous domain. Our method, referred to as Exponential Family Functional Principal Component Analysis (EFPCA), assumes the data are generated from an exponential family distribution, and the matrix of the canonical parameters has a low‐rank structure. The proposed method flexibly accommodates not only the standard one‐way functional data, but also two‐way (or bivariate) functional data. In addition, we introduce a new cross validation method for estimating the latent rank of a generalized data matrix. We demonstrate the efficacy of the proposed methods using a comprehensive simulation study. The proposed method is also applied to a real application of the UK mortality study, where data are binomially distributed and two‐way functional across age groups and calendar years. The results offer novel insights into the underlying mortality pattern. | - |
dc.language | eng | - |
dc.publisher | Wiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1541-0420 | - |
dc.relation.ispartof | Biometrics | - |
dc.rights | Preprint This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Postprint This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. | - |
dc.subject | Functional principal component analysis | - |
dc.subject | Generalized linear model | - |
dc.subject | Mortality study | - |
dc.subject | Singular value decomposition | - |
dc.subject | Two‐way functional data | - |
dc.title | Exponential Family Functional data analysis via a low‐rank model | - |
dc.type | Article | - |
dc.identifier.email | Shen, H: haipeng@hku.hk | - |
dc.identifier.authority | Shen, H=rp02082 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1111/biom.12885 | - |
dc.identifier.pmid | 29738627 | - |
dc.identifier.scopus | eid_2-s2.0-85046539137 | - |
dc.identifier.hkuros | 308043 | - |
dc.identifier.volume | 74 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 1301 | - |
dc.identifier.epage | 1310 | - |
dc.identifier.isi | WOS:000457779100017 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0006-341X | - |