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http://hdl.handle.net/1942/16193
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DC Field | Value | Language |
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dc.contributor.author | BLOMMAERT, Adriaan | - |
dc.contributor.author | HENS, Niel | - |
dc.contributor.author | Beutels, Ph | - |
dc.date.accessioned | 2014-01-29T12:57:30Z | - |
dc.date.available | 2014-01-29T12:57:30Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | COMPUTATIONAL STATISTICS & DATA ANALYSIS, 71, p. 667-680 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | http://hdl.handle.net/1942/16193 | - |
dc.description.abstract | Penalized generalized estimating equations with Elastic Net or L2-Smoothly Clipped Absolute Deviation penalization are proposed to simultaneously select the most important variables and estimate their effects for longitudinal Gaussian data when multicollinearity is present. The method is able to consistently select and estimate the main effects even when strong correlations are present. In addition, the potential pitfall of time-dependent covariates is clarified. Both asymptotic theory and simulation results reveal the effectiveness of penalization as a data mining tool for longitudinal data, especially when a large number of variables is present. The method is illustrated by mining for the main determinants of life expectancy in Europe. (C) 2013 Elsevier B.V. All rights reserved. | - |
dc.description.sponsorship | We thank the associate editor and referees for their constructive comments. This research was funded by the University of Antwerp's concerted research action number 23405 (BOF-GOA). Niel Hens was funded by the UA Scientific Chair in Evidence Based Vaccinology. | - |
dc.language.iso | en | - |
dc.subject.other | Covariate selection; Generalized estimating equations; Longitudinal data; Multicollinearity; Penalization; Time-dependent covariates | - |
dc.title | Data mining for longitudinal data under multicollinearity and time dependence using penalized generalized estimating equations | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 680 | - |
dc.identifier.spage | 667 | - |
dc.identifier.volume | 71 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Blommaert, A (reprint author), Univ Pl 1 S4-11, BE-2610 Antwerp, Belgium. adriaan.blommaert@ua.ac.be; Niel.Hens@uhasselt.be; Philippe.Beutels@ua.ac.be | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1016/j.csda.2013.02.023 | - |
dc.identifier.isi | 000328869000050 | - |
item.fullcitation | BLOMMAERT, Adriaan; HENS, Niel & Beutels, Ph (2014) Data mining for longitudinal data under multicollinearity and time dependence using penalized generalized estimating equations. In: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 71, p. 667-680. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2015 | - |
item.contributor | BLOMMAERT, Adriaan | - |
item.contributor | HENS, Niel | - |
item.contributor | Beutels, Ph | - |
item.accessRights | Restricted Access | - |
crisitem.journal.issn | 0167-9473 | - |
crisitem.journal.eissn | 1872-7352 | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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blommaert 1.pdf Restricted Access | Published version | 471.57 kB | Adobe PDF | View/Open Request a copy |
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