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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
  • 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
  • Examples Gallery

Examples Gallery#

Here are lots of examples, you can follow the examples to do what you want to do.

Linear Model and its Variants

Generalized Linear Models

Survival Models

Fusion Models

Graphical Models

Miscellaneous

  • 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
  • 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

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1. Linear Model and its Variants

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