PMID- 34682072
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20211026
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 23
IP  - 10
DP  - 2021 Oct 15
TI  - Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional 
      Data Application.
LID - 10.3390/e23101348 [doi]
LID - 1348
AB  - In 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.
FAU - Opoku, Eugene A
AU  - Opoku EA
AD  - Department of Mathematics and Statistics, University of Victoria, Victoria, BC 
      V8P 5C2, Canada.
FAU - Ahmed, Syed Ejaz
AU  - Ahmed SE
AD  - Department of Mathematics and Statistics, Brock University, St. Catharines, ON 
      L2S 3A1, Canada.
FAU - Nathoo, Farouk S
AU  - Nathoo FS
AD  - Department of Mathematics and Statistics, University of Victoria, Victoria, BC 
      V8P 5C2, Canada.
LA  - eng
PT  - Journal Article
DEP - 20211015
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC8534815
OTO - NOTNLM
OT  - LASSO estimation
OT  - asymptotic bias and risk
OT  - high-dimensional data
OT  - linear mixed model
OT  - multicollinearity
OT  - pretest and shrinkage estimation
OT  - ridge estimation
COIS- The authors declare no conflict of interest.
EDAT- 2021/10/24 06:00
MHDA- 2021/10/24 06:01
PMCR- 2021/10/15
CRDT- 2021/10/23 01:10
PHST- 2021/09/09 00:00 [received]
PHST- 2021/10/08 00:00 [revised]
PHST- 2021/10/12 00:00 [accepted]
PHST- 2021/10/23 01:10 [entrez]
PHST- 2021/10/24 06:00 [pubmed]
PHST- 2021/10/24 06:01 [medline]
PHST- 2021/10/15 00:00 [pmc-release]
AID - e23101348 [pii]
AID - entropy-23-01348 [pii]
AID - 10.3390/e23101348 [doi]
PST - epublish
SO  - Entropy (Basel). 2021 Oct 15;23(10):1348. doi: 10.3390/e23101348.