Robust transformation mixed-effects models for longitudinal continuous proportional data
dc.contributor.author | Zhang, Peng | en_US |
dc.contributor.author | Qiu, Zhenguo | en_US |
dc.contributor.author | Fu, Yuejiao | en_US |
dc.contributor.author | Song, Peter X.-K. | en_US |
dc.date.accessioned | 2009-07-06T15:40:49Z | |
dc.date.available | 2010-08-02T17:56:56Z | en_US |
dc.date.issued | 2009-06 | en_US |
dc.identifier.citation | Zhang, Peng; Qiu, Zhenguo; Fu, Yuejiao; Song, Peter X.-K. (2009). "Robust transformation mixed-effects models for longitudinal continuous proportional data." Canadian Journal of Statistics 37(2): 266-281. <http://hdl.handle.net/2027.42/63085> | en_US |
dc.identifier.issn | 0319-5724 | en_US |
dc.identifier.issn | 1708-945X | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/63085 | |
dc.description.abstract | The authors propose a robust transformation linear mixed-effects model for longitudinal continuous proportional data when some of the subjects exhibit outlying trajectories over time. It becomes troublesome when including or excluding such subjects in the data analysis results in different statistical conclusions. To robustify the longitudinal analysis using the mixed-effects model, they utilize the multivariate t distribution for random effects or/and error terms. Estimation and inference in the proposed model are established and illustrated by a real data example from an ophthalmology study. Simulation studies show a substantial robustness gain by the proposed model in comparison to the mixed-effects model based on Aitchison's logit-normal approach. As a result, the data analysis benefits from the robustness of making consistent conclusions in the presence of influential outliers. The Canadian Journal of Statistics © 2009 Statistical Society of Canada | en_US |
dc.format.extent | 301651 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | John Wiley & Sons, Inc. | en_US |
dc.subject.other | Statistics | en_US |
dc.subject.other | Mathematics and Statistics | en_US |
dc.title | Robust transformation mixed-effects models for longitudinal continuous proportional data | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, USA | en_US |
dc.contributor.affiliationother | Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2G1 ; Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2G1. | en_US |
dc.contributor.affiliationother | Division of Cancer Epidemiology, Prevention and Screening, Alberta Health Services, Edmonton, Alberta, Canada T5J 3H1 | en_US |
dc.contributor.affiliationother | Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada M3J 1P3 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/63085/1/10015_ftp.pdf | |
dc.identifier.doi | 10.1002/cjs.10015 | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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