<p>100 simulations were used to plot and evaluate the predictive performance of six variable selection methods when the number of total predictors (<i>t</i> = 100 and 200), the number of true predictors (<i>r</i> = 8, 12, 16 and 20) and sample size (<i>n</i> = 50, 100, 200, 300 and 500) change. Left panel: the number of total predictors <i>t</i> = 100; right panel: the number of total predictors <i>t</i> = 200. Six compared variable selection methods: stepwise, stability selection, LASSO, Bolasso, two-stage hybrid and bootstrap ranking procedures.</p
A: Number of true predictors that had a non-zero coefficient estimate (Oracle = 5). B: Number of fal...
In public health and in applied research in general, analysts frequently use automated variable sele...
Variable selection is one of the important practical issues for many scientific engineers. Although ...
<p>100 simulations were used to plot and evaluate the predictive performance of the six variable sel...
<p>100 simulations were used to plot and evaluate the predictive performance of six variable selecti...
<p>Asymptotic analysis of the metric AUC was used to evaluate the predictive performance of the six ...
<p>The six compared variable selection methods: stepwise, stability selection, LASSO, Bolasso, two-s...
A new version of the False Selection Rate variable selection method of Wu, Boos, and Stefanski (2007...
Abstract Background Variable selection is frequently carried out during the analysis of many types o...
Background: Variable selection is frequently carried out during the analysis of m...
<p>Variable selection results for scenario a, <i>k</i> = 4, <i>p</i> = 200, and <i>n</i> = 100. Box-...
We propose a new approach to variable selection designed to control the false selection rate (FSR), ...
Most variable selection techniques focus on first-order linear regression models. Often, interaction...
<p>n = 50 (A, B), n = 110 (C, D) and n = 500 (E, F). A, C, E: solid line for sAIC, dashed line for s...
Motivation: With the growth of big data, variable selection has become one of the critical challenge...
A: Number of true predictors that had a non-zero coefficient estimate (Oracle = 5). B: Number of fal...
In public health and in applied research in general, analysts frequently use automated variable sele...
Variable selection is one of the important practical issues for many scientific engineers. Although ...
<p>100 simulations were used to plot and evaluate the predictive performance of the six variable sel...
<p>100 simulations were used to plot and evaluate the predictive performance of six variable selecti...
<p>Asymptotic analysis of the metric AUC was used to evaluate the predictive performance of the six ...
<p>The six compared variable selection methods: stepwise, stability selection, LASSO, Bolasso, two-s...
A new version of the False Selection Rate variable selection method of Wu, Boos, and Stefanski (2007...
Abstract Background Variable selection is frequently carried out during the analysis of many types o...
Background: Variable selection is frequently carried out during the analysis of m...
<p>Variable selection results for scenario a, <i>k</i> = 4, <i>p</i> = 200, and <i>n</i> = 100. Box-...
We propose a new approach to variable selection designed to control the false selection rate (FSR), ...
Most variable selection techniques focus on first-order linear regression models. Often, interaction...
<p>n = 50 (A, B), n = 110 (C, D) and n = 500 (E, F). A, C, E: solid line for sAIC, dashed line for s...
Motivation: With the growth of big data, variable selection has become one of the critical challenge...
A: Number of true predictors that had a non-zero coefficient estimate (Oracle = 5). B: Number of fal...
In public health and in applied research in general, analysts frequently use automated variable sele...
Variable selection is one of the important practical issues for many scientific engineers. Although ...