Variable selection in strong hierarchical semiparametric models for longitudinal data

Zeng, Xian-Bin; Ma, Shuangge; Qin, Yichen; Li, Yang

doi:10.4310/sii.2015.v8.n3.a9

Cited by 4 publications

(6 citation statements)

References 32 publications

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“…A two-stage Bayesian approach was proposed by Bryan et al, 13 but this method modeled more general forms of hierarchy and only considered Gaussian responses. Zeng et al 14 proposed a strong hierarchical additive partially linear model, or SHAPLM, which considers interactions between parametric and nonparametric terms in a longitudinal response setting, but only with Gaussian responses.…”

Section: Introductionmentioning

confidence: 99%

Efficient interaction selection for clustered data via stagewise generalized estimating equations

Vaughan

Aseltine

Chen

et al. 2020

Statistics in Medicine

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Model selection in the presence of interaction terms is challenging as the final model must maintain a hierarchy between main effects and interaction terms. This work presents two stagewise estimation approaches to appropriately select models with interaction terms that can utilize generalized estimating equations to model clustered data. The first proposed technique is a hierarchical lasso stagewise estimating equations approach, which is shown to directly correspond to the hierarchical lasso penalized regression. The second is a stagewise active set approach, which enforces the variable hierarchy by conforming the selection to a properly growing active set in each stagewise estimation step. The effectiveness in interaction selection and the superior computational efficiency of the proposed techniques are assessed in simulation studies. The new methods are applied to a study of hospitalization rates attributed to suicide attempts among 15 to 19 year old at the school district level in Connecticut.

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Section: Introductionmentioning

confidence: 99%

Efficient interaction selection for clustered data via stagewise generalized estimating equations

Vaughan

Aseltine

Chen

et al. 2020

Statistics in Medicine

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“…Accumulating evidences suggest that interactions may have important implications beyond the main effects. Extensive methodological development and data analysis have been conducted . Promising findings have been made for multiple business and industry problems …”

Section: Introductionmentioning

confidence: 99%

“…Extensive methodological development and data analysis have been conducted. [1][2][3] Promising findings have been made for multiple business and industry problems. 4,5 In high-dimensional interaction analysis, there are two generic paradigms.…”

Section: Introductionmentioning

confidence: 99%

Integrative interaction analysis using threshold gradient directed regularization

Qin

et al. 2018

Appl Stoch Models Bus & Ind

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For many complex business and industry problems, high‐dimensional data collection and modeling have been conducted. It has been shown that interactions may have important implications beyond the main effects. The number of unknown parameters in an interaction analysis can be larger or much larger than the sample size. As such, results generated from analyzing a single data set are often unsatisfactory. Integrative analysis, which jointly analyzes the raw data from multiple independent studies, has been conducted in a series of recent studies and shown to outperform single–data set analysis, meta‐analysis, and other multi–data set analyses. In this study, our goal is to conduct integrative analysis in interaction analysis. For regularized estimation and selection of important interactions (and main effects), we apply a threshold gradient directed regularization approach. Advancing from the existing studies, the threshold gradient directed regularization approach is modified to respect the “main effects, interactions” hierarchy. The proposed approach has an intuitive formulation and is computationally simple and broadly applicable. Simulations and the analyses of financial early warning system data and news‐APP (application) recommendation behavior data demonstrate its satisfactory practical performance.

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“…Other semiparametric models for longitudinal data in which variable selection has been investigated include partially linear varying coefficient models 35,36 and additive partial linear models. 37,38…”

Section: Introductionmentioning

confidence: 99%

Model selection in multivariate semiparametric regression

Hai

2017

Stat Methods Med Res

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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.

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Variable selection in strong hierarchical semiparametric models for longitudinal data

Cited by 4 publications

References 32 publications

Efficient interaction selection for clustered data via stagewise generalized estimating equations

Efficient interaction selection for clustered data via stagewise generalized estimating equations

Integrative interaction analysis using threshold gradient directed regularization

Model selection in multivariate semiparametric regression

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