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Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification
tl;dr
An alternative initial weight in adaptive penalized logistic regression (CBPLR) is proposed. Expand
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  • Open Access
Adjusted Adaptive LASSO in High-Dimensional Poisson Regression Model
The LASSO has been widely studied and used in many applications, but it not shown oracle properties. Depending on a consistent initial parameters vector, an adaptive LASSO showed oracle properties,Expand
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  • Open Access
Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
tl;dr
Adaptive regularized logistic regression using the elastic net regularization using the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. Expand
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  • Open Access
A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives
Abstract A high-dimensional quantitative structure–activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a newExpand
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A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification
tl;dr
The common issues of high-dimensional gene expression data are that many of the genes may not be relevant, and there exists a high correlation among genes. Expand
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High Dimensional Logistic Regression Model using Adjusted Elastic Net Penalty
Reduction of the high dimensional binary classification data using penalized logistic regression is one of the challenges when the explanatory variables are correlated. To tackle both estimate theExpand
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  • Open Access
Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection
Reduction of the high dimensional classification using penalized logistic regression is one of the challenges in applying binary logistic regression. The applied penalized method, correlation basedExpand
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  • Open Access
Bayesian bridge quantile regression
tl;dr
We propose the Bayesian bridge for variable selection and coefficient estimation in quantile regression with the bridge penalty. Expand
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Developing a ridge estimator for the gamma regression model
The ridge regression model has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The gamma regression model is a very popular model inExpand
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Re-sampling in Linear Regression Model Using Jackknife and Bootstrap
Abstract Statistical inference is based generally on some estimates that are functions of the data. Resampling methods offer strategies to estimate or approximate the sampling distribution of aExpand
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