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http://hdl.handle.net/1942/37066
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DC Field | Value | Language |
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dc.contributor.author | VERHASSELT, Anneleen | - |
dc.contributor.author | FLOREZ POVEDA, Alvaro | - |
dc.contributor.author | Van Keilegeom, Ingrid | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.date.accessioned | 2022-03-29T14:09:18Z | - |
dc.date.available | 2022-03-29T14:09:18Z | - |
dc.date.issued | 2021 | - |
dc.date.submitted | 2022-03-17T13:41:13Z | - |
dc.identifier.citation | Journal of Statistical Research, 55 (1) , p. 43 -58 | - |
dc.identifier.uri | http://hdl.handle.net/1942/37066 | - |
dc.description.abstract | We investigate the performance of methods for estimating the conditional quantile of a response based on longitudinal data, when outcomes are incomplete and when the correlation between repeated responses is ignored. In a simulation study, we compare the performance of the quantile regression estimator based on the complete cases, the available cases, quantile-based multiple imputation, and quantile-based inverse probability weight-ing. In the data setting considered, quantile-based multiple imputation is the most promising method with the best bias-efficiency trade-off. A potential drawback, however, is its computation time. | - |
dc.language.iso | en | - |
dc.subject.other | and phrases: Dropout | - |
dc.subject.other | Inverse probability weighting | - |
dc.subject.other | Missing data | - |
dc.subject.other | Multiple imputation | - |
dc.subject.other | Quantile regression | - |
dc.title | The impact of incomplete data on quantile regression for longitudinal data | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 58 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 43 | - |
dc.identifier.volume | 55 | - |
local.bibliographicCitation.jcat | A2 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.type.programme | H2020 | - |
local.relation.h2020 | 694409 | - |
dc.identifier.doi | 10.47302/jsr.2021550105 | - |
local.provider.type | - | |
local.uhasselt.international | no | - |
item.contributor | VERHASSELT, Anneleen | - |
item.contributor | FLOREZ POVEDA, Alvaro | - |
item.contributor | Van Keilegeom, Ingrid | - |
item.contributor | MOLENBERGHS, Geert | - |
item.fulltext | With Fulltext | - |
item.fullcitation | VERHASSELT, Anneleen; FLOREZ POVEDA, Alvaro; Van Keilegeom, Ingrid & MOLENBERGHS, Geert (2021) The impact of incomplete data on quantile regression for longitudinal data. In: Journal of Statistical Research, 55 (1) , p. 43 -58. | - |
item.accessRights | Open Access | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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55n1_3.pdf | Published version | 243.05 kB | Adobe PDF | View/Open |
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