Choosing the optimal hybrid covariance estimators in adaptive elastic net regression models using information complexity
From MaRDI portal
Publication:5107503
DOI10.1080/00949655.2019.1647431OpenAlexW2966753957MaRDI QIDQ5107503
Publication date: 27 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2019.1647431
Monte Carlo simulationinformation complexityelastic netadaptive elastic nethybrid covariance estimators
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A well-conditioned estimator for large-dimensional covariance matrices
- A novel hybrid dimension reduction technique for undersized high dimensional gene expression data sets using information complexity criterion for cancer classification
- Estimating the dimension of a model
- Informational complexity criteria for regression models.
- Cancer classification using gene expression data.
- Dimension reduction strategies for analyzing global gene expression data with a response
- Big and complex data analysis. Methodologies and applications
- Shrinkage Algorithms for MMSE Covariance Estimation
- Double shrunken selection operator
- A new data adaptive elastic net predictive model using hybridized smoothed covariance estimators with information complexity
- Geometric Representation of High Dimension, Low Sample Size Data
- Regularization and Variable Selection Via the Elastic Net
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
This page was built for publication: Choosing the optimal hybrid covariance estimators in adaptive elastic net regression models using information complexity