Improving accuracy models using elastic net regression approach based on empirical mode decomposition
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Publication:5867445
DOI10.1080/03610918.2020.1728319zbMath1497.62181OpenAlexW3008540950MaRDI QIDQ5867445
Ahmed R. M. Alsayed, Mohd Tahir Ismail, Ahmad M. Awajan, Abdullah S. al-Jawarneh
Publication date: 14 September 2022
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2020.1728319
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)
Uses Software
Cites Work
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- Nearly unbiased variable selection under minimax concave penalty
- The Adaptive Lasso and Its Oracle Properties
- Subset selection in multiple linear regression in the presence of outlier and multicollinearity
- On the adaptive elastic net with a diverging number of parameters
- Lasso Regression Based on Empirical Mode Decomposition
- The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Hilbert–Huang Transform and Its Applications
- Regularization and Variable Selection Via the Elastic Net
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- How Do You Make A Time Series Sing Like a Choir? Extracting Embedded Frequencies from Economic and Financial Time Series using Empirical Mode Decomposition
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