scholarly journals A review of Bayesian variable selection methods: what, how and which

2009 ◽  
Vol 4 (1) ◽  
pp. 85-117 ◽  
Author(s):  
R. B. O'Hara ◽  
M. J. Sillanpää
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Selection Methods

Sparse Estimation and Uncertainty with Application to Subgroup Analysis

2017 ◽  
Vol 25 (1) ◽  
pp. 1-40 ◽  
Author(s):  
Marc Ratkovic ◽  
Dustin Tingley
Keyword(s):  
Variable Selection ◽  
Confidence Intervals ◽  
Public Support ◽  
Theoretical Work ◽  
Bayesian Variable Selection ◽  
Order Effects ◽  
Selection Methods ◽  
Sparse Estimation ◽  
Recent Theoretical Work ◽  
Subgroup Effects

We introduce a Bayesian method, LASSOplus, that unifies recent contributions in the sparse modeling literatures, while substantially extending pre-existing estimators in terms of both performance and flexibility. Unlike existing Bayesian variable selection methods, LASSOplus both selects and estimates effects while returning estimated confidence intervals for discovered effects. Furthermore, we show how LASSOplus easily extends to modeling repeated observations and permits a simple Bonferroni correction to control coverage on confidence intervals among discovered effects. We situate LASSOplus in the literature on how to estimate subgroup effects, a topic that often leads to a proliferation of estimation parameters. We also offer a simple preprocessing step that draws on recent theoretical work to estimate higher-order effects that can be interpreted independently of their lower-order terms. A simulation study illustrates the method’s performance relative to several existing variable selection methods. In addition, we apply LASSOplus to an existing study on public support for climate treaties to illustrate the method’s ability to discover substantive and relevant effects. Software implementing the method is publicly available in theRpackagesparsereg.

scholarly journals Bayesian Variable Selection Methods for Matched Case-Control Studies

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Josephine Asafu-Adjei ◽  
Mahlet G. Tadesse ◽  
Brent Coull ◽  
Raji Balasubramanian ◽  
Michael Lev ◽  
...  
Keyword(s):  
Variable Selection ◽  
Statistical Power ◽  
High Efficiency ◽  
Massachusetts General Hospital ◽  
Imaging Study ◽  
Bayesian Variable Selection ◽  
Case Control ◽  
Selection Methods ◽  
Case Control Studies ◽  
Matched Case

AbstractMatched case-control designs are currently used in many biomedical applications. To ensure high efficiency and statistical power in identifying features that best discriminate cases from controls, it is important to account for the use of matched designs. However, in the setting of high dimensional data, few variable selection methods account for matching. Bayesian approaches to variable selection have several advantages, including the fact that such approaches visit a wider range of model subsets. In this paper, we propose a variable selection method to account for case-control matching in a Bayesian context and apply it using simulation studies, a matched brain imaging study conducted at Massachusetts General Hospital, and a matched cardiovascular biomarker study conducted by the High Risk Plaque Initiative.

scholarly journals The value of Bayesian predictive projection for variable selection: An example selecting lifestyle predictors of young adult well-being

2021 ◽  
Author(s):  
Adam Bartonicek ◽  
Shay Ruby Wickham ◽  
Narun Pat ◽  
Tamlin S. Conner
Keyword(s):  
Young Adults ◽  
New Zealand ◽  
Variable Selection ◽  
Predictive Accuracy ◽  
Reference Model ◽  
Well Being ◽  
Bayesian Variable Selection ◽  
Compact Model ◽  
Selection Methods ◽  
Stepwise Selection

Abstract BackgroundVariable selection is an important issue in many fields such as public health and psychology. Researchers often gather data on many variables of interest and then are faced with two challenging goals: building an accurate model with few predictors, and making probabilistic statements (inference) about this model. Unfortunately, it is currently difficult to attain these goals with the two most popular methods for variable selection methods: stepwise selection and LASSO. The aim of the present study was to demonstrate the use predictive projection feature selection – a novel Bayesian variable selection method that delivers both predictive power and inference. We apply predictive projection to a sample of New Zealand young adults, use it to build a compact model for predicting well-being, and compare it to other variable selection methods. MethodsThe sample consisted of 791 young adults (ages 18 to 25, 71.7% female) from New Zealand who had taken part in the Dunedin Daily Life Study in 2013-2014. Participants completed a 13-day online daily diary assessment of their well-being and a range of lifestyle variables (e.g., sleep, physical activity, diet variables). The participants’ diary data was averaged across days and analyzed cross-sectionally to identify predictors of average flourishing. Predictive projection was used to select as few predictors as necessary to approximate the predictive accuracy of a reference model with all 28 predictors. Predictive projection was also compared to other variable selection methods, including stepwise selection and LASSO.ResultsThree predictors were sufficient to approximate the predictions of the reference model: higher sleep quality, less trouble concentrating, and more servings of fruit. The performance of the projected submodel generalized well. Compared to other variable selection methods, predictive projection lead to models with either matching or slightly worse performance but with much fewer predictors.ConclusionPredictive projection was used to efficiently arrive at a compact model with good predictive accuracy. The predictors selected into the submodel – felt refreshed after waking up, had less trouble concentrating, and ate more servings of fruit – were all theoretically meaningful. Our findings have important implications for applications of variable selection in health research.

scholarly journals The value of Bayesian predictive projection for variable selection: an example of selecting lifestyle predictors of young adult well-being

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
A. Bartonicek ◽  
S. R. Wickham ◽  
N. Pat ◽  
T. S. Conner
Keyword(s):  
Young Adults ◽  
New Zealand ◽  
Variable Selection ◽  
Predictive Accuracy ◽  
Reference Model ◽  
Well Being ◽  
Bayesian Variable Selection ◽  
Compact Model ◽  
Selection Methods ◽  
Stepwise Selection

Abstract Background Variable selection is an important issue in many fields such as public health and psychology. Researchers often gather data on many variables of interest and then are faced with two challenging goals: building an accurate model with few predictors, and making probabilistic statements (inference) about this model. Unfortunately, it is currently difficult to attain these goals with the two most popular methods for variable selection methods: stepwise selection and LASSO. The aim of the present study was to demonstrate the use predictive projection feature selection – a novel Bayesian variable selection method that delivers both predictive power and inference. We apply predictive projection to a sample of New Zealand young adults, use it to build a compact model for predicting well-being, and compare it to other variable selection methods. Methods The sample consisted of 791 young adults (ages 18 to 25, 71.7% female) living in Dunedin, New Zealand who had taken part in the Daily Life Study in 2013–2014. Participants completed a 13-day online daily diary assessment of their well-being and a range of lifestyle variables (e.g., sleep, physical activity, diet variables). The participants’ diary data was averaged across days and analyzed cross-sectionally to identify predictors of average flourishing. Predictive projection was used to select as few predictors as necessary to approximate the predictive accuracy of a reference model with all 28 predictors. Predictive projection was also compared to other variable selection methods, including stepwise selection and LASSO. Results Three predictors were sufficient to approximate the predictions of the reference model: higher sleep quality, less trouble concentrating, and more servings of fruit. The performance of the projected submodel generalized well. Compared to other variable selection methods, predictive projection produced models with either matching or slightly worse performance; however, this performance was achieved with much fewer predictors. Conclusion Predictive projection was used to efficiently arrive at a compact model with good predictive accuracy. The predictors selected into the submodel – felt refreshed after waking up, had less trouble concentrating, and ate more servings of fruit – were all theoretically meaningful. Our findings showcase the utility of predictive projection in a practical variable selection problem.

scholarly journals Dose–Response Modeling Under Simple Order Restrictions Using Bayesian Variable Selection Methods

2014 ◽  
Vol 6 (3) ◽  
pp. 252-262 ◽  
Author(s):  
Martin Otava ◽  
Ziv Shkedy ◽  
Dan Lin ◽  
Hinrich W. H. Göhlmann ◽  
Luc Bijnens ◽  
...  
Keyword(s):  
Variable Selection ◽  
Dose Response ◽  
Bayesian Variable Selection ◽  
Selection Methods ◽  
Order Restrictions ◽  
Simple Order ◽  
Response Modeling
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