Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application

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dc.contributor.authorOpoku, Eugene A.
dc.contributor.authorAhmed, Syed Ejaz
dc.contributor.authorNathoo, Farouk S.
dc.date.accessioned2021-11-01T18:13:40Z
dc.date.available2021-11-01T18:13:40Z
dc.date.copyright2021en_US
dc.date.issued2021
dc.descriptionResearch is supported by the Visual and Automated Disease Analytics (VADA) graduate training program.en_US
dc.description.abstractIn a host of business applications, biomedical and epidemiological studies, the problem of multicollinearity among predictor variables is a frequent issue in longitudinal data analysis for linear mixed models (LMM). We consider an efficient estimation strategy for high-dimensional data application, where the dimensions of the parameters are larger than the number of observations. In this paper, we are interested in estimating the fixed effects parameters of the LMM when it is assumed that some prior information is available in the form of linear restrictions on the parameters. We propose the pretest and shrinkage estimation strategies using the ridge full model as the base estimator. We establish the asymptotic distributional bias and risks of the suggested estimators and investigate their relative performance with respect to the ridge full model estimator. Furthermore, we compare the numerical performance of the LASSO-type estimators with the pretest and shrinkage ridge estimators. The methodology is investigated using simulation studies and then demonstrated on an application exploring how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThis research was funded by Natural Sciences and Engineering Research Council of Canada (NSERC).en_US
dc.identifier.citationOpoku, E. A., Ahmed, S. E., & Nathoo, F. S. (2021). Sparse estimation strategies in linear mixed effect models for high-dimensional data application. entropy, 23(1348), 1-24. https://doi.org/10.3390/e23101348en_US
dc.identifier.urihttps://doi.org/10.3390/e23101348
dc.identifier.urihttp://hdl.handle.net/1828/13475
dc.language.isoenen_US
dc.publisherentropyen_US
dc.subjectlinear mixed model
dc.subjectridge estimation
dc.subjectpretest and shrinkage estimation
dc.subjectmulticollinearity
dc.subjectasymptomatic bias and risk
dc.subjectLASSO estimation
dc.subjecthigh-dimensional data
dc.subject.departmentDepartment of Mathematics and Statistics
dc.titleSparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Applicationen_US
dc.typeArticleen_US

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