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Research Article

Variable selection using the EM and CEM algorithms in mixtures of linear mixed models

ORCID Icon & ORCID Icon
Pages 2196-2231 | Received 11 Feb 2022, Accepted 30 Jan 2023, Published online: 15 Feb 2023

References

  • Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;19(6):716–723.
  • Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–464.
  • Mallows CL. Some comments on Cp. Technometrics. 2000;42(1):87–94.
  • Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B (Methodol). 1996;58(1):267–288.
  • Fan J, Li R. Variable selection via nonconcave penalized likelihood and its Oracle properties. J Am Stat Assoc. 2001;96(456):1348–1360.
  • Zou H. The adaptive Lasso and its Oracle properties. J Am Stat Assoc. 2006;101(476):1418–1429.
  • Antoniadis A. Wavelets in statistics: a review. J Ital Stat Soc. 1997;6(2):97–130.
  • Fan J. Comments on wavelets in statistics: a review by A. Antoniadis. J Ital Stat Soc. 1997;6(2):131.
  • Bakin S. Adaptive regression and model selection in data mining problems [Doctoral dissertation]. Canberra: Australian National University; 1999.
  • Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B (Stat Methodol). 2006;68(1):49–67.
  • Friedman J, Hastie T, Tibshirani R. A note on the group Lasso and a sparse group Lasso. arXiv preprint arXiv:1001.0736. 2010.
  • Simon N, Friedman J, Hastie T, et al. A sparse-group Lasso. J Comput Graph Stat. 2013;22(2):231–245.
  • Wang H, Leng C. A note on adaptive group Lasso. Comput Stat Data Anal. 2008;52(12):5277–5286.
  • Wang L, Chen G, Li H. Group SCAD regression analysis for microarray time course gene expression data. Bioinformatics. 2007;23(12):1486–1494.
  • Khalili A. An overview of the new feature selection methods in finite mixture of regression models. J Iranian Stat Soc. 2011;10(2):201–235.
  • Raftery AE, Dean N. Variable selection for model-based clustering. J Am Stat Assoc. 2006;101(473):168–178.
  • Maugis C, Celeux G, Martin-Magniette M-L. Variable selection for clustering with Gaussian mixture models. Biometrics. 2009;65(3):701–709.
  • Khalili A, Chen J. Variable selection in finite mixture of regression models. J Am Stat Assoc. 2007;102(479):1025–1038.
  • Khalili A, Chen J, Lin S. Feature selection in finite mixture of sparse normal linear models in high-dimensional feature space. Biostatistics. 2011;12(1):156–172.
  • Tang Q, Karunamuni R. Robust variable selection for finite mixture regression models. Ann Inst Stat Math. 2018;70(3):489–521.
  • Yu C, Wang X. A new model selection procedure for finite mixture regression models. Commun Stat Theory Methods. 2020;49(18):4347–4366.
  • Du Y, Khalili A, Nešlehová JG, et al. Simultaneous fixed and random effects selection in finite mixture of linear mixed-effects models. Can J Stat. 2013;41(4):596–616.
  • Lee K-J, Chen R-B. Bayesian variable selection in a finite mixture of linear mixed-effects models. J Stat Comput Simul. 2019;89(13):2434–2453.
  • Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodol). 1977;39(1):1–38.
  • Novais L, Faria S. Comparison of the EM, CEM and SEM algorithms in the estimation of finite mixtures of linear mixed models: a simulation study. Comput Stat. 2021;36(4):2507–2533.
  • Celeux G, Govaert G. A classification EM algorithm for clustering and two stochastic versions. Comput Stat Data Anal. 1992;14(3):315–332.
  • Stone M. Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Ser B (Methodol). 1974;36(2):111–133.
  • Craven P, Wahba G. Smoothing noisy data with spline functions. Numer Math. 1978;31(4):377–403.
  • Novais L, Faria S. Selection of the number of components for finite mixtures of linear mixed models. J Interdiscip Math. 2021;24(8):2237–2268.
  • Sclove SL. Application of model-selection criteria to some problems in multivariate analysis. Psychometrika. 1987;52(3):333–343.
  • Cavanaugh JE. A large-sample model selection criterion based on Kullback's symmetric divergence. Stat Probab Lett. 1999;42(4):333–343.
  • Cavanaugh JE. Criteria for linear model selection based on Kullback's symmetric divergence. Aust N Z J Stat. 2004;46(2):257–274.
  • Neyman J, Pearson ES. On the use and interpretation of certain test criteria for purposes of statistical inference: Part I. Biometrika. 1928;20A:175–240.
  • Neyman J, Pearson ES. On the use and interpretation of certain test criteria for purposes of statistical inference: Part II. Biometrika. 1928;20A:263–294.
  • Benaglia T, Chauveau D, Hunter D, et al. Mixtools: an R package for analyzing finite mixture models. J Stat Softw. 2009;32(6):1–29.
  • Survey Research Center. Panel Study of Income Dynamics [public use dataset]. 2017.
  • Faraway JJ. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Boca Raton, FL: CRC Press; 2006.

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