Search alternatives:
selection algorithm » detection algorithm (Expand Search), detection algorithms (Expand Search), prediction algorithms (Expand Search)
mixed selection » mixed selective (Expand Search), model selection (Expand Search), based selection (Expand Search)
Showing 1 - 20 results of 107 for search 'mixed selection algorithm', query time: 0.26s Refine Results
  1. 1
    Variable selection for microbiome data.

    Variable selection for microbiome data. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  2. 2
    Variable selection for GWAS data.

    Variable selection for GWAS data. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  3. 3
    Variable selection for gene expression data.

    Variable selection for gene expression data. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  4. 4
    Runtime of fitting algorithm (for single λ) on each of three real omics data sets.

    Runtime of fitting algorithm (for single λ) on each of three real omics data sets. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  5. 5
    True versus fitted (standardized) BMI from BGLR model and our model (HighDimMixedModels with SCAD penalty) with and without random effects included.

    True versus fitted (standardized) BMI from BGLR model and our model (HighDimMixedModels with SCAD penalty) with and without random effects included. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  6. 6
    Variable selection for GWAS data with <i>q</i> = 1 or 5.

    Variable selection for GWAS data with <i>q</i> = 1 or 5. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  7. 7
    Variable selection for microbiome data with “scale-free” and “band” structure and <i>q</i> ∈ {1, 5}.

    Variable selection for microbiome data with “scale-free” and “band” structure and <i>q</i> ∈ {1, 5}. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  8. 8
    Decision maker preference and scheme selection.

    Decision maker preference and scheme selection. by Yuhang Wang (332111)

    Published 2025
    “…The improved NSGA-II algorithm is used to solve the Pareto front efficiently, and the search efficiency is improved by the elite reservation strategy and the congestion adaptive adjustment mechanism. …”
    Save to List
    Saved in:
  9. 9
    Gene expression simulation settings.

    Gene expression simulation settings. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  10. 10
    Non-zero estimated gene effects in riboflavin data.

    Non-zero estimated gene effects in riboflavin data. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  11. 11
    Variable selection of final model fits in all gene expression simulation settings with <i>q</i> = 3 random effects.

    Variable selection of final model fits in all gene expression simulation settings with <i>q</i> = 3 random effects. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  12. 12
    Variance component estimation for gene expression data.

    Variance component estimation for gene expression data. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  13. 13
    Effect estimation for GWAS data with <i>q</i> = 1 or 5.

    Effect estimation for GWAS data with <i>q</i> = 1 or 5. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  14. 14
    Variance component estimation for GWAS data.

    Variance component estimation for GWAS data. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  15. 15
    Effect estimation for microbiome data with “scale-free” and “band” structure and <i>q</i> ∈ {1, 5}.

    Effect estimation for microbiome data with “scale-free” and “band” structure and <i>q</i> ∈ {1, 5}. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  16. 16
    Effect estimation for GWAS data with <i>q</i> = 3.

    Effect estimation for GWAS data with <i>q</i> = 3. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  17. 17
    Effect estimation for microbiome data with “scale-free” and “band” structure and <i>q</i> = 3.

    Effect estimation for microbiome data with “scale-free” and “band” structure and <i>q</i> = 3. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  18. 18
    Effect estimation for gene expression data.

    Effect estimation for gene expression data. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  19. 19
    Compensation in LASSO-based estimation in presence of inter-feature correlation.

    Compensation in LASSO-based estimation in presence of inter-feature correlation. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  20. 20
    The standard deviations across simulated data sets of non-zero estimates of penalized coefficients under different omics studies.

    The standard deviations across simulated data sets of non-zero estimates of penalized coefficients under different omics studies. by Evan Gorstein (20553314)

    Published 2025
    Subjects:
    Save to List
    Saved in:

Search Tools: