A new variable selection approach using Random Forests

2013 ◽  
Vol 60 ◽  
pp. 50-69 ◽  
Author(s):  
A. Hapfelmeier ◽  
K. Ulm
Keyword(s):  
Variable Selection ◽  
Random Forests ◽  
Selection Approach

scholarly journals Variable selection with missing data in both covariates and outcomes: Imputation and machine learning

2021 ◽  
pp. 096228022110463
Author(s):  
Liangyuan Hu ◽  
Jung-Yi Joyce Lin ◽  
Jiayi Ji
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Variable Selection ◽  
Random Forests ◽  
Gradient Boosting ◽  
Stepwise Selection ◽  
Machine Learning Methods ◽  
Extreme Gradient Boosting ◽  
Selection Approach ◽  
Additive Regression

Variable selection in the presence of both missing covariates and outcomes is an important statistical research topic. Parametric regression are susceptible to misspecification, and as a result are sub-optimal for variable selection. Flexible machine learning methods mitigate the reliance on the parametric assumptions, but do not provide as naturally defined variable importance measure as the covariate effect native to parametric models. We investigate a general variable selection approach when both the covariates and outcomes can be missing at random and have general missing data patterns. This approach exploits the flexibility of machine learning models and bootstrap imputation, which is amenable to nonparametric methods in which the covariate effects are not directly available. We conduct expansive simulations investigating the practical operating characteristics of the proposed variable selection approach, when combined with four tree-based machine learning methods, extreme gradient boosting, random forests, Bayesian additive regression trees, and conditional random forests, and two commonly used parametric methods, lasso and backward stepwise selection. Numeric results suggest that, extreme gradient boosting and Bayesian additive regression trees have the overall best variable selection performance with respect to the [Formula: see text] score and Type I error, while the lasso and backward stepwise selection have subpar performance across various settings. There is no significant difference in the variable selection performance due to imputation methods. We further demonstrate the methods via a case study of risk factors for 3-year incidence of metabolic syndrome with data from the Study of Women’s Health Across the Nation.

Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

2019 ◽  
Author(s):  
Sierra Bainter ◽  
Thomas Granville McCauley ◽  
Tor D Wager ◽  
Elizabeth Reynolds Losin
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Limited Information ◽  
Online Application ◽  
Stochastic Search Variable Selection ◽  
Selection Approach ◽  
Pain Ratings ◽  
Research Questions ◽  
Standard Techniques ◽  
Search Variable

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.

scholarly journals Identifying Activity-sensitive Spectral Lines: A Bayesian Variable Selection Approach

2019 ◽  
Vol 158 (5) ◽  
pp. 210
Author(s):  
Bo Ning ◽  
Alexander Wise ◽  
Jessi Cisewski-Kehe ◽  
Sarah Dodson-Robinson ◽  
Debra Fischer
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Spectral Lines ◽  
Selection Approach

A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling

The Analyst ◽  
2014 ◽  
Vol 139 (19) ◽  
pp. 4836 ◽  
Author(s):  
Bai-chuan Deng ◽  
Yong-huan Yun ◽  
Yi-zeng Liang ◽  
Lun-zhao Yi
Keyword(s):  
Variable Selection ◽  
Binary Matrix ◽  
Matrix Sampling ◽  
Selection Approach ◽  
Variable Space

Is the Way Forward to Step Back? Documenting the Frequency with which Study Goals are Misaligned with Study Methods and Interpretations in the Epidemiologic Literature

2021 ◽  
Author(s):  
Katrina L Kezios
Keyword(s):  
Variable Selection ◽  
Random Sample ◽  
Research Study ◽  
Research Process ◽  
Study Methods ◽  
Selection Approach ◽  
The Way

Abstract In any research study, there is an underlying research process that should begin with a clear articulation of the study’s goal. The study’s goal drives this process; it determines many study features including the estimand of interest, the analytic approaches that can be used to estimate it, and which coefficients, if any, should be interpreted. “Misalignment” can occur in this process when analytic approaches and/or interpretations do not match the study’s goal; misalignment is potentially more likely to arise when study goals are ambiguously framed. This study documented misalignment in the observational epidemiologic literature and explored how the framing of study goals contributes to its occurrence. The following misalignments were examined: 1) use of an inappropriate variable selection approach for the goal (a “goal-methods” misalignment) and 2) interpretation of coefficients of variables for which causal considerations were not made (e.g., Table 2 Fallacy, a “goal-interpretation” misalignment). A random sample of 100 articles published 2014-2018 in the top 5 general epidemiology journals were reviewed. Most reviewed studies were causal, with either explicitly stated (13/103, 13%) or associationally-framed (71/103, 69%) aims. Full alignment of goal-methods-interpretations was infrequent (9/103, 9%), although clearly causal studies (5/13, 38%) were more often fully aligned than seemingly causal ones (3/71, 4%). Goal-methods misalignments were common (34/103, 33%), but most frequently, methods were insufficiently reported to draw conclusions (47/103, 46%). Goal-interpretations misalignments occurred in 31% (32/103) of studies and occurred less often when the methods were aligned (2/103, 2%) compared with when the methods were misaligned (13/103, 13%).

scholarly journals Sparse statistical health monitoring: A novel variable selection approach to diagnosis and follow-up of individual patients

2017 ◽  
Vol 164 ◽  
pp. 83-93 ◽  
Author(s):  
J. Engel ◽  
L. Blanchet ◽  
U.F.H. Engelke ◽  
R.A. Wevers ◽  
L.M.C. Buydens
Keyword(s):  
Variable Selection ◽  
Health Monitoring ◽  
Selection Approach

scholarly journals Gene selection: a Bayesian variable selection approach

2003 ◽  
Vol 19 (1) ◽  
pp. 90-97 ◽  
Author(s):  
K. E. Lee ◽  
N. Sha ◽  
E. R. Dougherty ◽  
M. Vannucci ◽  
B. K. Mallick
Keyword(s):  
Variable Selection ◽  
Gene Selection ◽  
Bayesian Variable Selection ◽  
Selection Approach

scholarly journals TSGSIS: a high-dimensional grouped variable selection approach for detection of whole-genome SNP–SNP interactions

2017 ◽  
Vol 33 (22) ◽  
pp. 3595-3602 ◽  
Author(s):  
Yao-Hwei Fang ◽  
Jie-Huei Wang ◽  
Chao A Hsiung
Keyword(s):  
Variable Selection ◽  
High Dimensional ◽  
Whole Genome ◽  
Selection Approach

scholarly journals Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models

2018 ◽  
Vol 67 (4) ◽  
pp. 813-839 ◽  
Author(s):  
Anna Bonnet ◽  
Céline Lévy‐Leduc ◽  
Elisabeth Gassiat ◽  
Roberto Toro ◽  
Thomas Bourgeron
Keyword(s):  
Variable Selection ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
High Dimensional ◽  
Heritability Estimation ◽  
Selection Approach
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