The Mnet method for variable selection

Huang, Jian; Breheny, Patrick; Lee, Sangin; Ma, Shuangge; Zhang, Cun Hui

doi:10.5705/ss.202014.0011

Cited by 45 publications

(61 citation statements)

References 22 publications

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“…However, when a group of variables have high correlations, the LASSO, MCP, and SCAD may select only one of the variables, leading to possibly inferior prediction performance than the ridge regression. 6,7 In medical research, individual biomarker may have useful but limited power to predict clinical outcomes. For instance, the accuracy of urine IL-18 and urine NGAL for diagnosis of severe AKI is moderate.…”

Section: Penalized Generalized Linear Regressionmentioning

confidence: 99%

“…A reparameterization is often convenient for implementation: ϕ = λ 1 + λ 2 and α = λ 1 / ϕ . 7,8 Therefore, for a fixed α value, we can compute solutions for a decreasing sequence of ϕ . Specifically, for the candidate values of ϕ , we consider a decreasing sequence of ϕ k , k = 1, …, K from ϕ max to ϕ min on the log scale with ϕ min = εϕ max .…”

Section: Parameter Estimationmentioning

confidence: 99%

“…With multicollinearity as such, different models may fit the data equally well, but they provide quite different model structures. 7,12 …”

Section: Simulation Studymentioning

confidence: 99%

“…6 Following the same idea, the mixture of MCP and ridge penalty (Mnet) has been developed for linear regression. 7 With count data, some variable selection methods have been proposed. The Enet has been incorporated in the generalized linear model (GLM) including Poisson regression.…”

Section: Introductionmentioning

confidence: 99%

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Penalized count data regression with application to hospital stay after pediatric cardiac surgery

Wang

Zappitelli

et al. 2016

Stat Methods Med Res

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Pediatric cardiac surgery may lead to poor outcomes such as acute kidney injury (AKI) and prolonged hospital length of stay (LOS). Plasma and urine biomarkers may help with early identification and prediction of these adverse clinical outcomes. In a recent multi-center study, 311 children undergoing cardiac surgery were enrolled to evaluate multiple biomarkers for diagnosis and prognosis of AKI and other clinical outcomes. LOS is often analyzed as count data, thus Poisson regression and negative binomial (NB) regression are common choices for developing predictive models. With many correlated prognostic factors and biomarkers, variable selection is an important step. The present paper proposes new variable selection methods for Poisson and NB regression. We evaluated regularized regression through penalized likelihood function. We first extend the elastic net (Enet) Poisson to two penalized Poisson regression: Mnet, a combination of minimax concave and ridge penalties; and Snet, a combination of smoothly clipped absolute deviation (SCAD) and ridge penalties. Furthermore, we extend the above methods to the penalized NB regression. For the Enet, Mnet, and Snet penalties (EMSnet), we develop a unified algorithm to estimate the parameters and conduct variable selection simultaneously. Simulation studies show that the proposed methods have advantages with highly correlated predictors, against some of the competing methods. Applying the proposed methods to the aforementioned data, it is discovered that early postoperative urine biomarkers including NGAL, IL18, and KIM-1 independently predict LOS, after adjusting for risk and biomarker variables.

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Section: Penalized Generalized Linear Regressionmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

“…With multicollinearity as such, different models may fit the data equally well, but they provide quite different model structures. 7,12 …”

Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Penalized count data regression with application to hospital stay after pediatric cardiac surgery

Wang

Zappitelli

et al. 2016

Stat Methods Med Res

Self Cite

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“…In the future, it is a potential direction for us to try different penalty functions proved to be useful in the one-step estimation procedures, like the MNet penalty [19] and the weight fused elastic-net penalty [20,21] for dealing with highly correlated variables in high-dimensional regression problems.…”

Section: Discussionmentioning

confidence: 99%

Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection

Xiao

2015

Journal of Statistical Computation and Simulation

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Regression and variable selection in high-dimensional settings, especially when p n has been a popular research topic in statistical machine learning. In recent years, many successful methods have been developed to tackle this problem. In this paper, we propose the multi-step adaptive elastic-net (MSAEnet), a multi-step estimation algorithm built upon adaptive elastic-net regularization. The numerical study on simulation data and real-world biological data sets have shown that the MSA-Enet method tends to significantly reduce the number of false-positive variables, while still maintain the estimation accuracy. By analysing the variables eliminated in each step, more insight could be gained about the structure of the correlated variable groups. These properties are desirable in many real-world variable selection and regression problems.

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Marginal false discovery rate control for likelihood‐based penalized regression models

Miller

Breheny

2019

Biometrical J

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The popularity of penalized regression in high‐dimensional data analysis has led to a demand for new inferential tools for these models. False discovery rate control is widely used in high‐dimensional hypothesis testing, but has only recently been considered in the context of penalized regression. Almost all of this work, however, has focused on lasso‐penalized linear regression. In this paper, we derive a general method for controlling the marginal false discovery rate that can be applied to any penalized likelihood‐based model, such as logistic regression and Cox regression. Our approach is fast, flexible and can be used with a variety of penalty functions including lasso, elastic net, MCP, and MNet. We derive theoretical results under which the proposed method is valid, and use simulation studies to demonstrate that the approach is reasonably robust, albeit slightly conservative, when these assumptions are violated. Despite being conservative, we show that our method often offers more power to select causally important features than existing approaches. Finally, the practical utility of the method is demonstrated on gene expression datasets with binary and time‐to‐event outcomes.

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The Mnet method for variable selection

Cited by 45 publications

References 22 publications

Penalized count data regression with application to hospital stay after pediatric cardiac surgery

Penalized count data regression with application to hospital stay after pediatric cardiac surgery

Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection

Marginal false discovery rate control for likelihood‐based penalized regression models

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