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  • 1. Linear Model and its Variants
    • 1.1. Linear Regression
    • 1.2. Linear regression with square-root loss
    • 1.3. Robust regression
    • 1.4. Quantile regression and expectile regression
    • 1.5. Linear mixed model
    • 1.6. Non-negative least squares
    • 1.7. Isotonic Regression
  • 2. Generalized Linear Models
    • 2.1. Logistic regressions
    • 2.2. Poisson regression
    • 2.3. Gamma regression
    • 2.4. Multiple response linear regression
    • 2.5. Multinomial logistic regression
    • 2.6. Multiple response non-negativity identity link Poisson model
  • 3. Survival Models
    • 3.1. Aalen’s additive hazards model
    • 3.2. Cox’s proportional hazards model
    • 3.3. Multivariate failure time model
    • 3.4. Competing risk model
  • 4. Fusion Models
    • 4.1. 1D trend filtering
    • 4.2. Piecewise-linear trend filtering with periodic components
    • 4.3. Spatial trend filtering
    • 4.4. DFS-Graph-Trend-Filtering
  • 5. Graphical Models
    • 5.1. Sparse Gaussian Precision Matrix
    • 5.2. Sparse Precision Matrix
    • 5.3. Sparse Ising Model
  • 6. Miscellaneous
    • 6.1. Non-linear feature selection via HSIC-SCOPE
    • 6.2. Portfolio selection
    • 6.3. Correlation inference for compositional data
    • 6.4. Classification on imbalanced labels with focal loss
    • 6.5. Exemplar-based clustering
  • 7. Sparsity Level Selection
    • 7.1. Supported Information Criteria and Cross Validation
  • Examples Gallery

2. Generalized Linear Models#

  • 2.1. Logistic regressions
  • 2.2. Poisson regression
  • 2.3. Gamma regression
  • 2.4. Multiple response linear regression
  • 2.5. Multinomial logistic regression
  • 2.6. Multiple response non-negativity identity link Poisson model

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1.7. Isotonic Regression

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2.1. Logistic regressions
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