Lasso regularization-based distributions of FDR and FPR for variable selection performed across 500 individual model fits to permuted and balanced wildland fire occurrence datasets.</p
The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated ...
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in scie...
2018-11-07Model selection in regression techniques has risen to the forefront in recent statistical ...
Symbol × marks a changepoint point in sorted Rank(X) values where variables falling to the right of ...
We develop a novel covariate ranking and selection algorithm for regularized ordinary logistic regre...
In high-dimensional data, the performance of various classiers is largely dependent on the selection...
Variable selection is of crucial significance in QSAR modeling since it increases the model predicti...
We begin with a few historical remarks about what might be called the regularization class of statis...
In high-dimensional data, the performance of various classifiers is largely dependent on the selecti...
Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern...
<p>Histogram showing the predictive distributions (probability mass functions) on the number of fall...
Researchers and data analysts are sometimes faced with the problem of very small samples, where the ...
The abundance of available digital big data has created new challenges in identifying relevant varia...
Predictive habitat models are an important tool for ecological research and conservation. A major ca...
The regularization methods were implemented with the Lasso, SCAD and MCP penalties.</p
The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated ...
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in scie...
2018-11-07Model selection in regression techniques has risen to the forefront in recent statistical ...
Symbol × marks a changepoint point in sorted Rank(X) values where variables falling to the right of ...
We develop a novel covariate ranking and selection algorithm for regularized ordinary logistic regre...
In high-dimensional data, the performance of various classiers is largely dependent on the selection...
Variable selection is of crucial significance in QSAR modeling since it increases the model predicti...
We begin with a few historical remarks about what might be called the regularization class of statis...
In high-dimensional data, the performance of various classifiers is largely dependent on the selecti...
Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern...
<p>Histogram showing the predictive distributions (probability mass functions) on the number of fall...
Researchers and data analysts are sometimes faced with the problem of very small samples, where the ...
The abundance of available digital big data has created new challenges in identifying relevant varia...
Predictive habitat models are an important tool for ecological research and conservation. A major ca...
The regularization methods were implemented with the Lasso, SCAD and MCP penalties.</p
The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated ...
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in scie...
2018-11-07Model selection in regression techniques has risen to the forefront in recent statistical ...