Abstract:
:Variable selection in semiparametric mixed models for longitudinal data remains a challenge, especially in the presence of multiple correlated outcomes. In this paper, we propose a model selection procedure that simultaneously selects fixed and random effects using a maximum penalized likelihood method with the adaptive least absolute shrinkage and selection operator penalty. Through random effects selection, we determine the correlation structure among multiple outcomes and therefore address whether a joint model is necessary. Additionally, we include a bivariate nonparametric component, as approximated by tensor product splines, to accommodate the joint nonlinear effects of two independent variables. We use an adaptive group least absolute shrinkage and selection operator to determine whether the bivariate nonparametric component can be reduced to additive components. To implement the selection and estimation method, we develop a two-stage expectation-maximization procedure. The operating characteristics of the proposed method are assessed through simulation studies. Finally, the method is illustrated in a clinical study of blood pressure development in children.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Li Z,Liu H,Tu Wdoi
10.1177/0962280217690769subject
Has Abstractpub_date
2018-10-01 00:00:00pages
3026-3038issue
10eissn
0962-2802issn
1477-0334journal_volume
27pub_type
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pub_type: 杂志文章,评审
doi:10.1177/096228029200100303
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